Exprodat Blogs

4 Ways to Streamline Unconventional E&P Projects

Tue, 31 Jan 2012

GIS technology has been used in ‘conventional’ petroleum for many years and is now being used increasingly in the development of continuous ‘unconventional’ resource plays such as shale gas, shale oil and coal bed methane.

This makes a lot of sense – most (if not all) of these resources are located onshore and GIS’s ability to integrate satellite imagery, network analysis and hydrological analysis (traditional heartlands for GIS) with more traditional petroleum analysis techniques means that it is arguably even more important in unconventionals.

This blog looks at a 4 examples in the unconventionals petroleum sector where GIS technology can be extremely useful in streamlining complex workflows. So, in no particular order…

1. Play Analysis

In conventional ‘play chance’ or ‘common risk segment’ mapping, a geoscientist assigns chance of success (COS) values to key petroleum play elements (e.g. reservoir, source and seal) and then combines the data stack to generate probability maps for the play.

Figure 1 – Play chance mapping of the Ardley CBM play.

You can apply this technique to unconventionals too, using different petroleum play elements (due to the source, reservoir and seal effectively being the same formation) such as total organic carbon % (TOC), gas maturity (VRo), depth to formation and thickness of formation. Typically this data is taken from wells drilled in exploratory or pilot drilling phases.

The example above is taken from an analysis of Alberta’s coal bed methane prospectivity and employed Exprodat’s Team-GIS Segment Analyst software to generate the play chance maps. The software hides the complexity of the geospatial analysis with easy-to-use tools that have been designed specifically for geoscientists.

2. Acreage Analysis

Because GIS allows you to easily integrate multi-disciplinary asset data (e.g. geological, environmental, economic, network, infrastructure, environmental) within a single analysis it is perfect for evaluating and grading unconventional acreage opportunities, such as licenses or lease blocks.

Using GIS you can combine traditional ‘conventional’ block ranking criteria such as play prospectivity with other factors (such as access to water via transport networks or pipelines, hydrology for water management, and environmental data such as sensitive habitats) in order to define analysis criteria and weightings; rank acreage and company acreage positions; and ultimately identify new opportunities.

Figure 2 – Example of ranked Haynesville Shale play sections, near Shreveport, Louisiana.

Quantitatively ranking opportunities by analysing all the available data in this way can be complex, but this is bread and butter to GIS technology and using the right tools the analysis can be completed surprisingly quickly. This allows you to iterate your analysis results many times in order to improve decision quality way beyond that which would be achievable without geospatial analysis.

Exprodat’s Team-GIS Acreage Analyst software packages the complex geospatial processing workflows required in acreage analysis into an easy-to-use toolkit for acreage and portfolio analysis of unconventional resource areas.

3. Reserve Estimation

In unconventional developments you often need to know how much proven, possible and probable reserves can be declared in an area, based on preliminary drilling results from exploration or pilot wells. Drill spacing unit (DSU) grid-based reserve classification techniques are often used for this (under the guidelines set out in the Petroleum Resources Management System, or "PRMS"), and can be applied to developments using both vertical and horizontal drilling, especially where well coverage is sparse.

Figure 3 – Conceptual 1P, 2P and 3P areas used in coal bed methane (Guidelines for the Application of the PRMS, Barker 2008).

Due to its inherent spatial awareness, GIS technology allows you to easily calculate accurate reserve areas, as well as use buffering around producing wells to help estimate reserves. This is demonstrated by the recently updated Society of Petroleum Evaluation Engineers (SPEE) ‘Monograph 3 - Guidelines for the Practical Evaluation of Undeveloped Reserves in Resource Plays’ publication which includes a recommended reserve estimation methodology based on GIS technology.

Once generated, such reserve area polygons can be combined with raster-based reserve-in-place (e.g. gas-in-place) grids derived from preliminary drilling at pilot sites. Using spatial analysis of the grids you can then calculate estimated reserve volumes based on the gas-in-place raster, as well as license interest and recovery factor attribute data.

Exprodat’s Team-GIS Unconventionals Analyst software contains tools to estimate unconventional reserves based on both horizontal and vertical wells, and can be applied to shale gas, shale oil and coal seam gas developments.

4. Geospatial Well Pattern Optimisation

Again, due to its inherent ability to understand spatial relationships, GIS technology is being used increasingly in the unconventionals well pattern planning arena. Using GIS you can analyse multiple surface drilling constraints, ensuring that wells are not placed within specified distances of areas where drilling is not permitted, such as leases held by other companies, protected habitats, rivers, road , forests and urban areas.

Figure 4 – Example well pattern avoiding constraints and showing drainage efficiency metrics.

In addition, GIS’s unique spatial analytics enable you to optimise well patterns ensuring that they maximise the amount of resource being 'drained' by the wells within the pattern, while avoiding the surface drilling constraints.

Exprodat’s Team-GIS Unconventionals Analyst contains unique tools to design well patterns around multiple surface drilling constraints in order to maximise resource drainage efficiency.

Take-aways

The four upstream examples discussed above show that GIS is an extremely useful toolkit when planning unconventional exploration and development projects, emerging as a core technology for extracting shale gas, shale oil and coal bed methane reserves.

Figure 5 – Team-GIS tools for unconventional resource exploration and development.

Exprodat’s Team-GIS software has been designed so that geoscience teams can easily leverage the unique power of geospatial technology when working in unconventionals without having to re-invent the wheel. The products are designed to hide the complexity and jargon of the geospatial realm so that geoscientists don’t have to embark on a steep learning curve to master the tools.

If you’d like further information on how Exprodat can help your unconventional projects please contact us now.

Posted by Chris Jepps, Technical Director, Exprodat.

Managing Cultural Data with ModelBuilder and Python

Mon, 07 Nov 2011

Cultural data which E&P organisations rely on for exploration and commercial developments is ever-changing by nature. Status of leases change, as do company equities and stakes in existing and new blocks, number of wells and information about production.

Typically, cultural data is made available by the data suppliers as regular updates, often in an unstructured format, including shapefiles or file geodatabases, tables and .csv files, which need to be incorporated within the organisation's corporate spatial data repositories. Datasets also need to comply with internal standards, not only in terms of data format, but also in term of naming conventions (for both feature classes as well as attributes), coordinate reference system and language.

Figure 1 - Cultural data available in the public domain for the North Sea region visualised in ArcGIS Desktop

Tasks of this kind are quite repetitive and labour-intense, and need to be run on a regular, often monthly basis. This can become a real struggle especially for small E&P organisations, where the database management team may be really constrained in terms of resources. In such cases keeping track of where the data comes from, what data is current or obsolete, and ensuring consistency and overall data quality can become challenging.

At Exprodat we often help our clients to stay on top of all this. We typically take advantage of the powerful capabilities of Python scripting and Esri's ModelBuilder to help automate such monthly updates.

Let's take the example of an operator whose main interest is the North Sea region. Operators active in this geographic area can access a multiple data sources for cultural data, both in the public domain (provided by central government departments, often free-of-charge) as well as from commercial data sources (see Figure 2).

Figure 2 - Example data providers for cultural data in the North Sea region (commercial vendors have been omitted).

If you add to the number of sources the fact that each of them provides data in different formats, languages and coordinate reference systems (see Figure 3) it's easy to understand how things can get a bit messy.

Figure 3 - Text files, Excel files, shapefiles and file geodatabases are all data formats used to deliver cultural data to users.

The cultural data geoprocessing infrastructure discussed here aims at ensuring the following:

Consistency in feature class (and layer file) names, independent of the provider
Consistency in attribute fields names and aliases
Attributes are all in English
A common coordinate reference system is used for all feature classes
Metadata is kept up-to-date.

Data geoprocessing

The set of tools used to build the automated data update infrastructure we discuss here uses Esri's ModelBuilder to pre-process the datasets as they are downloaded from the providers' websites. Python scripting is then used to handle the heavier geoprocessing tasks such as translating attributes into English, renaming fields, setting aliases and re-projecting to the corporate coordinate reference system.

The datasets which are made available by some data providers do not have consistent names and in addition may change without notice, potentially on a month-by-month basis⁽¹⁾. ModelBuilder (Figure 4) has the flexibility to take into account the occurrence of these variations. By exposing the name of the datasets as a parameter within the models I can make sure that the datasets for which the name has changed are processed without the person looking after the data updates needing to:

Remember the specific bit of code that takes care of the update for that dataset,
Remember where this code is stored, and
Know which bit of the script needs to be changed.

This is particularly convenient when the required geoprocessing tools are native in ArcGIS, and already exposed as toolboxes.

Figure 4 - Example of the pre-processing Cultural Data Management toolbox and of one of the models

After the datasets have been pre-processed, Python scripting takes care of the most laborious tasks using advanced geoprocessing before the data is published to the corporate data repositories. Tasks like feature modification, attribute translation and creation of data archives are all tasks executed from within Python (Figure 5).

Figure 5 - Python scripting for advanced geoprocessing

Using python scripts allows you to schedule the processes to be run in batch mode during out-of-office hours, e.g. overnight or over the weekend. Windows Task Scheduler can be used to set up the sequence and the time when the scripts are run with no supervision. Log files are generated after each script is run, tracking the successful completion of the tasks or any problem that has been encountered.

A simple tool to ensure that metadata is also kept up-to-date completes the suite of tools. This tool parses the metadata files stored for each feature class/layer file at a given location in order to update the metadata, e.g. the dataset's "last update" date.

Results

We have had very positive feedback from the clients where we have deployed the solution discussed here as it is relatively easy to implement and maintain. The solution also allows flexibility in the sense that it can accommodate new datasets that providers may make available down the line, even after the whole framework is implemented. It also requires close to no maintenance and, an element that may be significant for smaller E&P organisations, does not require any additional third-party applications or ArcGIS plug-ins.

To give you an idea of how long it takes to run the monthly data update from the sources indicated in Figure 2 (public and commercial), here's some rough numbers:

Acquisition/download from data sources: 2 to 4 hours (depending on the actual number of datasets updates available from each data source)
Model-based data pre-processing: ½ to 1 hour
Python geoprocessing (Python): 3 hours (done overnight or at weekend, no man/effort).

Up-to-date cultural data can then be used by GIS users with confidence and consumed in any GIS client, e.g. ArcGIS Desktop (Figure 1) or ArcGIS Explorer Online (Figure 6).

Figure 6 - Cultural data available in the public domain for the North Sea region visualised in ArcGIS Explorer Online⁽²⁾

Please contact Exprodat using the link at the bottom of this post if you are interested in finding out more about the solution presented here.

Posted by Paola Peroni, Senior GIS Consultant.

Notes:

Some providers "attach" the date of the last data update (basically the cut for the vendor's database) to the name of the feature class. So, for instance a field dataset updated at "Date A" would bear the name "Fields_DateA" (not the real name), while the update made available at a following date would be provided, for instance, as "Fields_DateB".
ArcGIS Explorer Online is the ESRI free online mapping tool which can be used to explore, visualise and share spatial information online. The North Sea Oil and Gas Data map shown in Figure 6 has been compiled by Exprodat using freely available E&P cultural data.

Enhancing Imagery using ArcGIS

Tue, 25 Oct 2011

One of the many questions that I get asked when delivering a GIS training course is “how do you enhance images – online background mosaics are good for general display but in a frontier basin study area, how do you process images to enhance surface features?”

Most petroleum GIS users use remotely sensed imagery as an image back-drop ranging from a regional basin view to a high resolution image of a well pad. Online imagery, such as ArcGIS online, is excellent for quickly displaying image data but for some applications a background image just isn’t enough. Enhancement of a local image dataset would allow improved geology and geomorphology mapping. This is often carried out in specialist remote sensing software.

Many of the online mosaics datasets are compressed processed images which preclude them from being used for image processing. To enhance imagery you require the uncompressed raw image data.

In oil and gas we often find that, especially in exploration, organisations have access to the Spatial Analyst extension which has many generic geoprocessing raster analysis tools that can be used for image processing.

Colour composites

The online imagery mosaics, commonly using Landsat imagery, have been processed to show surface features representing a true view of the earth surface, simulating the earth surface viewed from space. You can create other types of image display using the Landsat imagery. It has six bands (nominally 30m per pixel) of multispectral image data where the sensor on the satellite measures the intensity of reflected light in six distinct bandwidths filters ranging from blue light to near infrared and short wave infrared. A colour composite is an image created using combinations of these image band data displayed in red, green and blue display channels. You are able to create true and false colour composites for general image enhancement (Drury 2001, Liu et al., 2009).

Landsat data is now freely available from the USGS (USGS, 2009). The data can be used in ArcGIS to enhance local image scenes resulting in improved classification and interpretation of regional geology and geomorphology.

Surface mapping

I'm using Landsat imagery of the Eastern Interior, Sultanate of Oman to demonstrate how ArcView and Spatial Analyst can be used to enhance geological and geomorphological features

The Huqf uplift is a window into the early Palaeozoic, where Cambrian through to Silurian rocks outcrop and are exposed. Here, the Cambrian Shuram – Buah facies outcrop and have been deformed by later Tertiary tectonics and faulting (Ries et al., 1990). The main tectonic features are the Saiwan – Nafun fault with associated Khufai and Buah anticlines.

The regional Landsat mosaic true colour composite (Fig.1a) clearly shows large scale features. However, this mosaic has been created using regional image statistics; the contrast between local features isn’t always clear and often the image appears “washed out”. In Fig. 1b a true colour composite of bands 321 displayed in RGB shows more contrast in surface features. Using the “from current display extent” further enhances the image display (Fig. 2).

Figure 1. a) The image on the left is a Landsat online mosaic from ESRI ArcGIS Online and b) on the right is a Landsat true colour composite.

Figure 2. Raster layer symbology with the statistics set to “From Current Display Extent”.

False colour composites (FCC) can be generated using different combinations of the Landsat bands displayed in red, green, blue (RGB) image display channels. An FCC generated using 531 display in RGB is a typical band combination used to map geology and geomorphology (Drury, 2001, Liu et al., 2009). In desert areas, a common FCC combination is 742 displayed in RGB (Fig. 4a and Drury, 2001). These colour composites allow improved interpretation of the local geology and geomorphology (Fig. 3b).

Figure 3. a) False colour composite 531 displayed in RGB and b) annotated image showing key geology features.

The Spatial Analyst extension has tools for carrying out multivariate analysis and classification which use image statistics to enhance the imagery. Principal component analysis (PCA) of the Landsat data further enhances surface features resulting in a more thematic classification. This image (Fig. 4b) when compared to the 531 and 742 false colour composites clearly improves the classification of surface features; the recent sand dunes in red tones, Khufai carbonate rocks in blue with an inner Abu Muhara sandstone in dark blue/purple, and sabhka / salt deposits in yellow. The PCA clearly shows the thin sand deposits to the south of the Khufai anticline located in Wadi Shuram..

Figure 4. a) False colour composite 531 displayed in RGB and b) principal component image display using PCA bands 124 displayed in RGB.

Enhanced images draped over an ASTER GDEM or SRTM DEM clearly show the relationship between surface geology and elevation and can be used to infer subsurface structure (or in the case of 2D or 3D seismic confirm connection of surface to sub-surface structure).

Figure 5. Perspective visualisation, orientated northwards, of the 531 false colour composite draped over an ASTER DEM using ArcScene.

What’s new in ArcGIS 10

The Image Analysis window and the Image Classification toolbar now allow easy access to enhancing imagery in ArcGIS with Spatial Analyst.

Summary

Geology and geomorphology mapping allows a glimpse into the location of basin edge exposures of the Cambrian source and Palaeozoic reservoir rocks in the Oman basin. Associated mapping of recent deposits and sands can help in seismic planning, logistics planning, HSE and downstream applications.

The tailored use of spatial analyst allows GIS users to enhance imagery without having specialist remote sensing software. This “first pass” image enhancement can help target areas for further specialist remote sensing applications and projects.

Understanding imagery and what the colours mean, is often not as straight forward as it seems. It’s becoming easier for the general GIS user to process and enhance imagery, but the observation skills needed to interpret the images are often overlooked. To aid interpretation, perspective viewing of the imagery draped on a suitable DEM not only produces the wow factor, but provides clear information on surface geological structure geometry and natural geomorphological barriers. This is invaluable when planning a new oil/gas pipeline or geology mapping field project.

To find out more on how use ArcGIS for terrain analysis, take a look at Exprodat’s Surface Analysis course.

Posted by Al Davis, Senior GIS Consultant, Exprodat.

References:

Drury, S. A., 2001, Image Interpretation in Geology, Routledge, third edition, pages 304.
Liu, J. G., and Mason, P. J., 2009, Essential Image Processing and GIS for Remote Sensing, Wiley-Blackwell, pages 460.
Pilcher, R., Roberts, G., Buckley, R., and Harbury, N., 1996, Structures within the Mahatta Humaid area, Huqf Uplift: implications for the tectonics of eastern Oman, Journal of African Earth Sciences, Volume 22, Issue 3, Pages 311-321
Ries, A. C.& Shackleton, R. M., 1990, Structures in the Huqf-Haushi Uplift, east Central Oman, Geological Society, London, Special Publications; 1990; v. 49; p. 653-663;
USGS, 2008, “USGS Landsat Imagery Release” http://landsat.usgs.gov/documents/USGS_Landsat_Imagery_Release.pdf

Sneak Preview Demos of TGUA

Tue, 18 Oct 2011

In my last blog I provided a sneak preview of Team-GIS Unconventionals Analyst (TGUA), our soon-to-be-released well planning and reserve forecasting ArcGIS Desktop extension for unconventional resource projects.

In this blog post I’d like to whet your appetite further by providing some links to short movies that we’ve put together demonstrating TGUA’s functionality.

Creating optimum unconventionals drilling patterns Example showing the use of Team-GIS software to optimise well drilling patterns for unconventional gas exploration and production.
Calculating accurate unconventional reserve areas Example showing the use of Team-GIS software to calculate areas of unconventional gas reserves based on well results.
Forecasting unconventional reserve volumes Example showing the use of Team-GIS software to forecast coal bed methane reserves based on a planned drilling campaign.

As mentioned in my previous post, TGUA was developed in partnership with a leading global integrated natural gas company, and provides significant and measurable business benefits. Drilling a single well can cost close to a million dollars, so using TGUA to reduce the number of wells can bring huge savings, not to mention environmental benefits.

In addition TGUA simplifies the process of calculating and forecasting compliant P1, P2 and un-booked reserves, ensuring accurate reporting to regulatory bodies.

The software is optimised for coalbed methane (CBM) or coal seam gas (CSG) projects, but can also be applied to shale gas applications.

For further details watch this space over the coming weeks, or contact gas@exprodat.com to find out more.

Posted by Chris Jepps, Technical Director, Exprodat.

Assessing Coal Bed Methane Acreage

Tue, 11 Oct 2011

As more and more oil and gas companies search for opportunities in the unconventionals sector, it is becoming increasingly critical to acquire good acreage. Charlotte Elliot, our 2011 Imperial College Petroleum Geoscience MSc student has recently spent a couple of months proving a workflow for doing just this - for the Candian province of Alberta. Her project aims were to identify the best coal bed methane (CBM) acreage based on an analysis of the key coal plays.

Read parts of her abstract below, and download her summary presentation at the end of the blog post...

"The study area lies within the Western Canadian Sedimentary Basin, a massive wedge of sedimentary rock extending from the Rocky Mountains in the west to the Canadian Shield in the east. Generally the stratigraphy contains large coal deposits, from Late Jurassic – Tertiary that accumulated in peat swamps and were consequentially covered by sediment derived from the Laramide and Columbian orogenies.

There are four main coal bearing formations across the area, the Ardley, the Horseshoe Canyon, the Belly River and the Mannville. Within these formations exist a number of plays with varying prospectivity.

Play fairway mapping (using Team-GIS Segment Analyst) allowed quick analysis of prospective areas. Utilizing existing 'proxy' layers sourced from the Geological Atlas of the Western Canada Sedimentary Basin, new play elements were created; (1) Coal Quality (2) Gas Quality (3) Coal Quantity and (4) Gas Quantity. These elements were turned into CRS and CCRS maps for each play to highlight areas of interest. Results showed that the Drumheller and Ardley plays showed the most potential for CBM production, having areas of 67,500 km² and 34,800 km² respectively within a zone of low-medium risk.

Further analysis produced a block ranking (using Team-GIS Acreage Analyst). As no official lease data was available for the area, an arbitrary grid was created instead. The blocks were scored according to the presence of plays within the block and their average GIP. Existing wells were examined to see how developed the areas were. Finally six blocks were identified in a northwest - southeast linear arrangement at the base of the foothills. These blocks may warrant further investigation for CBM production."

For a summary of the project's methodology and results, download Charlotte's MSc presentation (requires site login or registration).

Posted by Chris Jepps, Technical Director, Exprodat.

Tip 19: Using Buffers to Model Lateral Migration

Mon, 03 Oct 2011

We often use ArcGIS Desktop’s buffer tool simply to select areas within set distances from a feature or features we are interested in. However, buffering can be used in more complex ways such as to produce a set of concentric buffers – this can be useful when looking at suspected migration of petroleum away from mature kitchen areas.

In the example below, I have a scanned map of source maturity from the Jurassic, North Sea, showing areas for mature, immature and basement. The blue box is the extent of my area of interest.

For my petroleum play let’s assume that I am interested in areas of potential exploration up to a distance of 20km from the mature areas, where lateral migration of hydrocarbons may have occurred.

Using the Multiple Ring Buffer Tool

Using the ArcToolbox Multiple Ring Buffer tool (ArcToolbox > Analysis Tools > Proximity > Multiple Ring Buffer) I select distances out to 10km and 20km from the mature source areas represented by the oil mature polygons. I’m going to model 2 buffers so that I can assign different risks to the buffered zones when making my common risk segment maps, e.g. using Team-GIS Segment Analyst.

Now I set the multiple ring buffer layer display property (Layer Properties > Display) to 50% transparency, so the buffers can be seen overlain on the original map.

Using the Clip Tool

To complete the workflow, I need to exclude any basement areas that intersect my buffer zones, so that my result gives only potential 10km and 20km migration zones. For this I use the Clip tool.

First I need to select the polygons that I want to clip the buffer layer with - in this case the oil immature polygons. An easy way to do this is selecting them from the attribute table.

When these are selected, I use the clip tool (ArcToolbox > Analysis Tools > Extract > Clip) to clip the buffer zones using the source mature polygons.

The result produces just the areas I want to include in my analysis.

So now when I create my common risk segment maps I can assign a high probability of source presence to the core (bright green) areas (e.g. 100%), and asign lower probability values to the zones of potential lateral migration, with probablilty values decreasing with distance from the kitchen (e.g. 30% then 10%).

Posted by Mike Phillips, Senior Consultant, Exprodat.

Using GIS in Unconventionals Developments

Wed, 28 Sep 2011

Exciting times here at Exprodat as we prepare to release a new software tool for the “unconventionals” market. In anticipation of this I thought I’d provide a sneak preview of what’s coming…

Team-GIS Unconventionals Analyst (TGUA) is a well planning and reserve forecasting application for unconventional resource projects. TGUA was developed in partnership with a leading global integrated natural gas company and has been used on development projects for over a year, which is quite a stamp of approval. Like other Team-GIS products the application is an ArcGIS Desktop extension, and was developed to solve specific problems encountered when developing unconventional fields.

Drilling Constraint Analysis and Well Pattern Planning

TGUA has tools for generating an optimal drilling pattern for an area, taking into account the required orientation of the pattern, surface drilling constraints, and locations where it has been decided that a well must be placed. The tools allow field development teams to create multiple well patterns, and calculate an ’efficiency’ for each. Field developers can then select the most efficient well pattern and make modifications manually, re-calculating the efficiency afterwards to validate the changes.

Reserve Forecasting

When planning any development it is essential to know the reserve volumes available, so TGUA also has tools for estimating and forecasting reserves using a grid-based reserves classification method used by many operators to evaluate unconventional resources. This grid-based method divides the area into cells sized according to a drill space unit (DSU), with each cell in the grid assigned a reserve classification according to the result of the well at the centre of the grid and the cell's distance from the centre.

TGUA uses the grid-based reserves classifications to enable unconventional field planners to calculate estimated reserves volumes based on resource-in-place data and known well results (e.g. from pilot drilling). It can also optionally identify “bracket” reserves (grid cells that may be ‘booked’ as reserves due to being between cells with proven or probable reserves), which is a real time-saver.

This combination of well and reserve analysis enables companies planning unconventional gas projects to drill the most productive wells first by analysing how reserves will be added over time through a drilling campaign, based on a forecast drill date.

In summary, TGUA provides significant and measurable business benefits. Given that drilling a single well can cost close to a million dollars, using TGUA to reduce the number of wells can bring huge savings. TGUA also simplifies the process of calculating and forecasting unconventional reserves, ensuring compliant P1, P2 and un-booked reserves for accurate reporting to regulatory bodies.

Watch this space for further details on the release of TGUA over the next month or so, or contact gas@exprodat.com to find out more.

Update 18th Oct: Read my next blog post: Sneak Preview Demos of TGUA

Posted by Chris Jepps, Technical Director, Exprodat.

Tip 18: Selecting From Multiple Areas

Wed, 03 Aug 2011

Selecting features in ArcMap is usually pretty straightforward, particularly when there are only a few records in a dataset. In such cases, the required features can simply be selected either from the Attribute Table, or from the map by clicking on an individual feature or "rubber-banding" multiple features. But when using layers where the features are widely distributed, it can be trickier.

In this example, I have a dataset of thousands of wells, but I’m only interested in a small number in 2 discrete areas. The wells are part of a group layer called ‘Wells’, where red symbols represent Exploration wells and black symbols represent Development wells.

Let’s assume that I want to select wells from 2 different geographic areas of interest, as shown in the image below.

Here is one way to select the required wells.

1. Using the Select Features tool, drag a "rubber-band" selection around the larger group of wells. When selected the wells will appear highlighted.

2. Holding down the [Shift] key, repeat the same for the other area. As this area has just a single well location you can click on it individually or draw another rubber band around it to select it. Again, the well will appear highlighted once selected.

Exporting your selection to a new feature class

Let’s assume that you now want to have a layer that only has the selected wells in it. This is easy to do if you export the layer to a new feature class, as follows:

1. Save all well selections by choosing Data > Export Data, change the Export setting from “All features” to “Selected features” and save out to a File Geodatabase for the new Feature Class.

2. Since the new Feature Class will have default symbols you might want to import the symbols from the original Wells layer. To do this right click the new Feature Class and select Properties. On the Symbology tab click the Import button and select the original layer from the drop-down to import its symbology.

These simple steps result in the desired selections being saved as a new layer, retaining the symbology of the original dataset.

Posted by Mike Phillips, Senior Consultant, Exprodat.

Environmental and Seabed Constraints Analysis in ArcGIS

Tue, 26 Jul 2011

GIS for environmental data management and analysis in E&P

At Exprodat we recently carried out a review of GIS use in the environmental side of the oil and gas business with the participation of some of our clients. The results show that awareness of how GIS can support environmental data management and environmental analysis workflows is still at an early stage in many of the companies we reviewed . GIS is often perceived by environmental teams as the "territory" of a few specialists, who may not actually sit within these teams.

Given the outcome of our study, I thought it would be worth showing a typical environmental workflow, to demonstrate how ArcGIS can be used to support analysis tasks effectively and quickly.

Environmental and Seabed Constraints Analysis

Let's assume the following scenario: the permitting team within the HSE department needs to understand the environmental and seabed constraints that affect a given well location. Although in my scenario the location of the new well is offshore, the same analysis principle can be applied to any onshore location as well as other exploration activities such as planning a new seismic survey acquisition campaign.

For this new development the permitting manager needs to answer a few simple questions:

1) Is the location of the proposed well within environmental protection and other surface and/or seabed constrained areas?
2) What is the minimum and maximum distance from the proposed well location to any constrained areas?

For my example I chose to focus on the Baltic Sea, mainly due to the availability of public data sources suitable for the analysis. Figure 1 below shows the area of interest; data has been downloaded from the HELCOM (see note 1) map and data service website. Shipping lanes and wind farm permit datasets have been made up arbitrarily.

Figure 1 – Study area used in my test

A detailed map of the existing seabed and environmental constraints is shown in Figure 2 where the location of the proposed new well is also indicated. All my data is in vector format; as you can see there is a variety of constraints affecting the proposed well location (see note 2). The presence of overlapping polygons was a factor which had to be taken into account when implementing the constraint analysis workflow, as discussed below.

Figure 2 - Constraints for the proposed drilling location

I made the assumption that the permitting team only has access to ArcView and Spatial Analyst (see note 3).

Model Builder for constraint analysis

For my constraint analysis workflow I used the ArcGIS Model Builder, in order to provide a portable and reusable tool for constraint analysis. This could then be shared among users within the permitting team or used for similar analyses elsewhere.

The model implements the following sequence of logical steps:

Classify constraint features based on the location of the proposed well with respect to the boundaries of the corresponding polygons (features classified as "within" or "outside"). This task is accomplished by using the Spatial Join tool.
Define the study area (analysis carried out only for those features which are at a significant distance from the proposed drilling location) and use the Euclidean Distance tool to generate a grid of distance values from the well location within the study area (see Figure 3 below).
Calculate the relevant statistical values (min and max distances to the boundaries) for features where the proposed location falls "outside" the polygonal boundary of the existing constraints using the Zonal Statistics as Table tool.

Figure 3 – Use of the Euclidean Distance and the Zonal Statistics as Table tool to derive distances for polygons classified as “outside”

Calculate the relevant statistical values (min and max distances to the boundaries) for features where the proposed location falls "within" the polygonal boundary of the existing constraints by converting these features from polygon to polylines (I did this by using the "Write Features to Text File" script available in the Sample toolbox at 9.3.1.). Then convert the output back to a polyline feature class (by using a modified version of the "Create Features from Text" script available in the same toolbox).
Finally follow a similar process as described above to extract distances to the boundaries of constraints (Figure 4).

Figure 4 - Convert polygon features to polylines to derive distances for features classified as "within"

Results

The environmental and seabed constraint map is shown in Figure 5. Constraints within the output layer have been symbolised based on distance from the proposed drilling location and the closest features have been labelled based on their attribute (constraint type).

The attribute table for the output layer reports the minimum and maximum distances from the border of the polygonal feature.

In my scenario the constraint analysis allowed me to quickly extract the following information that needs to be accounted for when planning the well location:

The proposed location falls within a shipping lane, a chemical munitions dumpsite and a geo-hazard area ("within polygons").
Minimum distance from boundaries of the "within" polygons varies from about 900 m and up to 5000 m.
Various constraints are classified as "outside" polygons. The proposed well falls outside these areas.
Minimum distance from boundaries of the "outside" polygons varies from about 5000 to up to several kilometres.

Figure 5 – Environmental and seabed constraints analysis map

Conclusions

The constraint analysis workflow provides information which is useful in the early stages of the planning and development process, where data at a regional scale is used to support the environmental permitting process.

The model would allow a permitting team to analyse suggested locations for exploration and development activities (such as drilling and seismic surveys) with respect to the environmental and physical constraints at both regional and local level. The map shown in Figure 5 can easily be integrated into an environmental impact report or any other permitting document required in an application procedure.

In summary, I hope that I have shown how in an oil and gas organisation GIS technology can provide decision support beyond the geoscientists sitting within the Exploration department. As our review demonstrated, the key element is to communicate the value of GIS across an organisation, increasing awareness at both the technical as well as the managerial level.

Posted by Paola Peroni, Senior GIS Consultant

Note 1: The Helsinki Commission (HELCOM) works to protect the marine environment of the Baltic Sea from all sources of pollution through intergovernmental cooperation between Denmark, Estonia, the European Community, Finland, Germany, Latvia, Lithuania, Poland, Russia and Sweden. Some of the data made available by HELCOM and used within the context of this blog has been partially modified to better suit the scope of my test.

Note 2: The well location discussed here is arbitrary and unrelated to any real world development in the area.

Note 3: ArcGIS provides a Proximity Analysis toolbox which contains tools that can be used for calculating the minimum distance between a point and polygon features. However, the tools needed to accomplish the type of analysis discussed here (such as the "Near" tool) are only available in ArcInfo.

Correctly Aligning Features in ArcMap: 3

Fri, 22 Jul 2011

In my previous posts (part 1 and part 2), I described how ArcMap warns users that a coordinate transformation is required and some potential pitfalls to be aware of.

In this post, I will explain how to set a coordinate transformation in ArcMap.

How to Select a Coordinate Transformation

Open the Geographic Coordinate Systems Transformations dialog by either clicking on the Transformations button in the Geographic Coordinate Systems Warning dialog or in the Data Frame Properties dialog.

The Convert From list displays a list of geodetic datums on which the CRS properties of map layers are based, while the Convert Into dropdown displays the geodetic datum on which the CRS property of the data frame is based.

In the screenshot below, a coordinate transformation must be set from GCS_WGS_1984 to GCS_European_1950 (note there is also a map layer with a CRS property based on European Datum 1950 but obviously no transformation is required from GCS_European_1950 to GCS_European_1950 because they are the same CRS).

You must ensure a valid coordinate transformation is set between each pair of different geodetic datums by selecting a coordinate transformation in the Using dropdown.

As stated in part one, often, there will be a choice of coordinate transformations to select from, some more suitable than others depending upon factors such as accuracy, derivation and region of use. The latter concern is particularly important. The screenshot below shows there are many coordinate transformation options from GCS_WGS_1984 to GCS_European_1950, however only a certain number are valid, eg in the North Sea some options are valid only in certain areas of use, such as Germany Offshore or Netherlands Offshore.

The obvious question is how can the user select from the many transformation options when they do not know which are fit for purpose? Fortunately, many companies have geodetic expertise and standards to assist users make a selection. Failing that, the EPSG Geodetic Parameter Dataset is an excellent resource for researching valid coordinate transformations.

Also note, although coordinate transformation names would seem to indicate transformation direction is from European Datum 1950 to WGS 1984, in this case the coordinate transformation is reversible and ArcMap handles the direction automatically.

Change

The world is forever changing and this is also true for coordinate transformations, whether it is literally - in the case of a large earthquake - or simply if a company begins exploring in a frontier region that has ill-defined or aging geodetic standards. New coordinate transformations are always being derived and old ones deprecated. ArcGIS lists both valid and deprecated coordinate transformations so as to maintain backwards compatibility when using older geographic data but proceed with care - it gives no indication of whether a particular coordinate transformation has been deprecated or not!

Summary

In ArcGIS, the onus is on the user to understand their data, CRSs and coordinate operations for the regions in which they are working.
Users should not ignore or dismiss ArcGIS Warning dialogs - they are there for a reason!
Users should never click the ‘Don’t warn me again…’ checkboxes. If they have done so in the past, uncheck the Skip Datum Check setting in the ArcMap Advanced Settings Utility to ensure coordinate transformation warning dialogs display when necessary.
To ensure correct alignment of features in the map, users should make sure that a valid coordinate transformation is set in the data frame for each map layer whose CRS property is based on a different geodetic datum to that of the data frame.
Finally, this can be a very tricky and complex problem with potentially important impacts so, if in doubt, do seek expert assistance!

Posted by Ian Milligan, Senior Consultant, Exprodat.

Correctly Aligning Features in ArcMap: 2

Wed, 20 Jul 2011

In my last post (part 1), I described two scenarios when ArcMap undertakes coordinate operations, commonly known as “on-the-fly projection”. In the second scenario, the user must select a valid coordinate transformation to ensure map layers are correctly aligned.

In this post, I will outline how ArcMap informs the user a coordinate transformation is required and discuss some pitfalls.

ArcMap Warning Dialogs

There are potential issues due to the way ArcGIS displays warning dialogs, the sometimes counter-intuitiveness of the user interface for selecting coordinate transformations and the fact ArcGIS does not strictly enforce coordinate transformation integrity.

1. Adding a New Map Layer

When adding a new map layer that has a CRS property based on a different geodetic datum to that of the data frame, the Geographic Coordinate Systems Warning dialog opens. This warns that one or more coordinate transformations should be selected by the user to achieve correct alignment on the map. It also displays a list of map layers which require transforming and their CRS properties:

Without strict enforcement of coordinate transformation integrity, the temptation for the end user is to click Close, plough on regardless and ignore alignment errors altogether, resulting in potentially mis-aligned layers within the map.

2. Changing the CRS Property of the ArcMap Data Frame

A different warning dialog opens when changing the CRS property of the data frame to a CRS based on a different geodetic datum to that used by one or more map layers:

Again, the temptation for the end user is to ignore the warning and plough on regardless.

Pitfalls - The ‘Don’t Warn me’ Checkboxes

Furthermore, both dialogs have two checkboxes that prevent warning dialogs from opening again during the current ArcMap session or - most alarmingly - ever again! There is a very real danger of users ticking one or both and, in all likelihood, thereafter creating maps containing positional errors.

You may ask, “Why doesn’t ArcGIS just enforce integrity?”. Well, many ArcGIS customers will, quite simply, not be concerned about this, whether it be because they work in a common CRS (eg local government in England work only with the British National Grid) or only undertake regional mapping. For this reason, Esri provide a mechanism to ignore potential coordinate transformation integrity issues.

However, the oil and gas industry frequently requires spatial analysis and mapping with a high degree of precision and positional accuracy. The possibility that warnings of potential coordinate transformation integrity issues have been disabled is a significant issue.

In my final post (part 3), I will explain how to set coordinate transformations in ArcGIS.

Posted by Ian Milligan, Senior Consultant, Exprodat.

Correctly Aligning Features in ArcMap: 1

Mon, 18 Jul 2011

As mentioned in a previous blog, ArcMap can correctly align layers of geographic data with different spatial reference (also called ‘coordinate reference system’ or ‘CRS’) properties within the same data frame. This so-called “on-the-fly projection” leaves source data unchanged – it does not permanently change it in any way whatsoever.

Using this functionality, companies avoid retaining multiple copies of the same geographic data with different CRS properties and this is a key benefit of using GIS software to manage and visualise geographic data.

However, inexperienced end users who are unaware how ArcMap undertakes on-the-fly projection coordinate operations may, quite reasonably, assume it will automatically align map layers in the data frame but this is not always true.

There are two primary use cases to consider:

Case one - map contains geographic data based on the same geodetic datum
Case two - map contains geographic data based on different geodetic datums

Case one – data based on the same geodetic datum

In this first case, the data frame and/or one or more map layers contained within it have different CRS properties that are based on the same geodetic datum. ArcMap needs only undertake coordinate conversions (often referred to a “projecting” or “un-projecting” the data) to correctly align features in different map layers.

Coordinate conversions are based on relatively accurate, unambiguous mathematical rules which ArcMap follows to convert geographic data in the data frame with relatively high accuracy.

Providing the required coordinate conversions are supported within ArcGIS (and most, but not all, are supported – see the ArcGIS Desktop Help for a comprehensive listing), ArcMap will automatically undertake the conversions and correct alignment is achieved.

So, in this case, ArcMap will handle the coordinate conversion automatically.

Case two – data based on different geodetic datums

In this second case, the data frame and/or one or more map layers contained within it have CRS properties that are based on different geodetic datums. ArcMap must undertake coordinate transformations (sometimes referred to as “datum transformations”) - possibly in addition to coordinate conversions - to correctly align features in different map layers.

Coordinate transformations are derived empirically using a set of common locations on different geodetic datums and therefore the choice, allocation, quality and number of points used can affect their accuracy.

In ArcGIS, the user must select the most suitable coordinate transformations (one transformation per map layer to be transformed) for correct alignment to be achieved in ArcMap. Often, there is a choice of coordinate transformations to select from, some more suitable than others depending upon their accuracy, derivation, region of use and fitness for purpose.

So, in this case, the onus is on the user to select a suitable coordinate transformation.

Potential Errors

So, why is this so important? Let’s consider a real world example of wells in the central North Sea. The CRS property of the data frame is ED50/UTM Zone 31N and a coordinate transformation has not been set.

The CRS property of the black wells is European Datum 1950 (GCS_European_1950 in ArcGIS, commonly referred to by the alias ‘ED50’) and they are correctly aligned. They have been converted automatically to ED50/UTM Zone 31N by ArcMap because only a coordinate conversion is required (see case one above).

The CRS property of the red wells is WGS 1984 (GCS_WGS_1984 in ArcGIS) and they are incorrectly aligned. They have not been transformed to ED50/UTM Zone 31N because the coordinate transformation has not been set in the data frame by the user.

Interestingly, the mis-alignment has positioned wells 30/24-34, 30/24-37 and 30/24-39 over the southern block boundary into block 30/29.

In part 2, I will explain when coordinate transformations should be set and some things to watch out for when doing this in ArcGIS.

Posted by Ian Milligan, Senior Consultant, Exprodat.

Making Movies with ArcGIS Explorer

Wed, 15 Jun 2011

In the plenary session of Esri's PUG Conference 2011 they showed a video from Anadarko discussing GIS and introducing their 'iMaps' web-based GIS solution. In the video Anadarko explained that their philosophy was to provide ArcGIS Desktop for GIS-savvy users, and the web-based GIS 'iMaps' for basic use across the enterprise.

Choosing the right tools across the enterprise

Nothing new in that, really - at Exprodat we've been advocating a similar approach for as long as we've been working with GIS. For a slice of (pre-ArcGIS Server) Exprodat history check out the slide below from c. 2004 - nice colours!

However, Anadarko then mentioned that they saw a requirement for a third level - for those people who needed more than iMaps could deliver but who couldn't handle ArcGIS Desktop. Their suggestion was to use ArcGIS Explorer Desktop (AGX). I thought it was a pretty interesting idea, and the extensible framework that AGX provides would certainly allow them to add custom functionality to it (such as our own AGX add-in for extruding data in 3D via KML).

ArcGIS Explorer presentation

Coinidentally I'd been thinking about putting together an eye-catching display to run during trade shows. I wanted to replace the rather uninspiring PowerPoint slide shows that we'd been using, so I decided to try building an AGX presentation as an excuse to look at the latest version at that time, v1500.

In just a few hours I put together an AGX presentation showing some of the outputs produced by our Team-GIS software tools, and, in conjunction with Camtasia Studio, created a video presentation called Viewing Team-GIS results in ArcGIS Explorer.

During the process I noticed a few things about AGX which I'll just mention briefly in case anyone else wants to try to create a similar presentation:

Anadarko suggested that users new to AGX would probably require some training, and I agree. It's not 100% intuitive across the board and has a few eccentricities.
AGX appeared to occasionally drop layers from my presentation, meaning I had to reload them and recreate some presentation 'slides'.
I found it confusing that the 'show popup' tool (equivalent to ArcGIS Desktop's Identify tool) sometimes wasn't available to me, depending on the format of layer I was using.
AGX seems prone to the odd crash, mostly when trying to display larger data layers like the rasters I was using. It did this even when I wasn't using any of the basemap services. However, I got round this by continually saving my project and studiously re-caching everything before I started the presentation.

In summary, and as I hope my own movie demonstrates, if you want to put a cool GIS presentation together then ArcGIS Explorer is well worth a look.

Posted by Chris Jepps, Technical Director, Exprodat.

Using the ArcGIS Correlation Coefficient

Wed, 08 Jun 2011

Investigating the Correlation Between Formation Pressure and Depth using ArcGIS

Determining whether or not two or more continuous variables are correlated in space or time and quantifying the nature and the strength of this correlation is a type of data analysis which is common in many fields including physical geography, environmental studies and earth sciences. In geology, for instance, it's been used to evaluate the relationship between stratigraphic horizons and the recent surface topography to provide insights on crustal movements caused by subsurface geological structures and faults¹.

A simple way to investigate the degree of dependency of two continuous variables is to use an index called the Correlation Coefficient, which expresses in basic terms (a number between -1 and 1) the nature (negative, positive or no relation) and strength of the linear relationship between phenomena across space and time. In order to compute the Correlation Coefficient various statistical parameters (such as mean and standard deviation) are required.

As a means of testing the capability of ArcGIS Desktop to automate this computation for variables relevant in exploration of natural resources, I put together a model using Model Builder in ArcToolbox. The idea is to automate the sequence of steps to compute the statistics parameters within a chosen neighbourhood shape and size for any two continuous variables which are linearly correlated.

In the results discussed below you can see how easily such tasks can be automated using ArcGIS Desktop. The workflow can be applied to a variety of analysis and modelling tasks and is not, necessarily, confined to the world of oil and gas exploration.

A bit of more context, the data I used and the workflow I followed for my test.

My example represents a pretty common task for geoscientists, and deals with investigating the correlation between the values of the formation pressure (Variable 1) measured at some level within a known (or potential) reservoir and the depth to the top of the horizon (Variable 2) - ideally the contact between the reservoir and the seal. In my case I wanted to build a "proof-of-concept" test focusing on a relatively small scale, to have a feeling for whether ArcGIS could be used to carry out more regional-scale analysis to identify, for instance, areas of anomalies in pressure values.

In ideal conditions, such as having petrophysically homogeneous reservoir and seal with no faults, I would expect the reservoir formation pressure and depth to the top of the reservoir to be correlated. A deviation from this behaviour would indicate the presence of possible anomalies which could suggest, for instance, local permeability changes, lithological barriers or changes in the quality of the top seal.

For my test I used a subset of data from the well-known Rotliegend gas play in the Dutch sector of the southern North Sea, therefore investigating only a small part of the gas system. The Rotliegend play comprises the prolific Westphalian source rocks for gas, the thick Slochteren sandstone reservoir, and a seal of Zechstein salt (Glennie & Provan,1990). Formation pressure and the modelled horizons for this play are in the public domain, and can be downloaded from the Netherlands Oil and Gas portal². Note that there is an increase in the depth of the top of the Slochteren Formation from SE to NW, with structural lows determined by the fault system affecting this area (see Figure 1 below).

Figure 1 - Location of my test area. Note the structural lows indicating a sudden drop in the depth to top Slochteren Formation. Use of the General QQ Plot tool to explore the nature of the relationship between formation pressure and depth to the top of the geological horizon is shown on the left.

Because the Correlation Coefficient assumes linearity of the relationship among phenomena, the first task in my workflow is to check whether this is true for my two variables. To do this I extrapolated the pressure measured at various depths at my well locations up to the top of the Slochteren Formation reservoir. Not having access to the value of the pressure gradient for each well, I arbitrarily assumed that the vertical variation of the pressure is constant throughout my reservoir³. This assumption is obviously gross simplification, although it does not undermine the validity of the general approach.

I computed the depth to top Slochteren from the depth to its base and its thickness. From this grid I extracted top depth values at the well locations where I had measured pressure and used my assumed constant pressure gradient to extrapolate the pressure at the top of the reservoir. Following this approach I ended up with two point datasets.

I then used the ArcGIS Geostatistical Analyst General QQ Plot tool to investigate the nature of the relationship between my two variables. The plot is shown in Figure 1. Note that the plot indicates a fairly linear relationship between depth and pressure. In such a case I can use the Correlation Coefficient to represent a statistically reliable measure of the degree of dependency among my phenomena. Following the linearity check, I interpolated point values to generate a raster surface for the formation pressure at the top of the reservoir.

The model

At this point of my workflow I obtained the input datasets my model needed to compute the Correlation Coefficient.

I had previously created a toolbox in ArcGIS called Pressure Analysis Tools containing two models (Figure 2A). The one called 'Preprocessing' simply prepares the data for the correlation analysis (which is needed in my case as the spatial extent and the cell sizes of my raster datasets are different). The actual correlation analysis is carried out by the one called 'Calculate Correlation Coefficient' (see an extract in Figure 2B).

Figure 2A - ArcGIS custom toolbox.

Figure 2B - ArcGIS model.

As you can appreciate from Figure 2B, the model uses the concept of focal statistics to compute the statistical parameters needed to derive the correlation grid. Focal statistics computes various statistical parameters for a defined neighbourhood shape and size which is repeatedly "moved" throughout the study area. See the ArcGIS online help for further information about using focal statistics in ArcGIS.

The output of my analysis is the result of a moving-window computation of the statistical significance of the relationship between the depth and the pressure at the top of my reservoir. Given that the correlation is computed within each moving window, the use of a focal statistics approach has the advantage of being able to take into account local variations in the degree of dependency across the region of interest.

Results

The result of the correlation analysis for my study area is shown in Figure 3.

Figure 3 - Correlation test between depth and pressure at the top of the Slochteren Formation.

As you can see the correlation map created show areas of high positive correlation (dark green) alternating with areas of high negative correlation (dark purple).

The positive correlation pattern is strong especially in the eastern part of my study area (area A in Figure 3), where my data indicates a transition between high and low pressure zones. It's interesting to note that the area of the structural low highlighted in Figure 1 (area B in Figure 3) shows high positive correlation with the pressure, whose values increases sharply in line with the sudden deepening of the top of the reservoir.

Throughout my study region pressure and depth generally show a strong relationship, either positive or negative: anomalous areas in which there is no relationship between these two variables are limited (lightest areas). This suggests that depth to the reservoir is indeed one of the factors which control pressure values for this specific stratigraphic unit.

Clearly my analysis misses a number of other complex, key elements which the geoscientist may want to take into account (fault system, burial depth, fluid overpressure values, location of salt structures in the Zechstein sealing horizon, etc.), but hopefully you can see how GIS technology provides powerful tools to carry out and automate analysis workflows.

Posted by Paola Peroni, Senior Consultant, Exprodat.

Lehne' R. and Siroko F., (2005): "Quantification of recent movement potentials in Schleswig-Holstein (Germany) by GIS-based calculation of correlation coefficients" - International Journal of Earth Science (2005) 94: 1094-1102.
The datasets I have used for this test are freely downloadable from the NLOG Oil and Gas data portal. Formation pressure values are available in a simple Excel spread sheet format, while depth to the base and the thickness of various horizons of interest for Oil and Gas exploration and production are available in grid format, ready to be used in ArcGIS.
In my test I only used the pressure values for the water in the pores of the reservoir. Other fluids overpressure such as gas, oil, condensate or any mix of these was not considered.

Tip 17: Reselecting Highlighted Features

Fri, 03 Jun 2011

In ArcGIS Desktop selecting features is often an essential step before copying, modifying attributes, changing geometry, or merging features. But when using layers with multiple overlapping polygons it’s often not easy to select the polygon you want.

Let’s assume that I have a layer with many overlapping polygons and I want to select a particular one, e.g. the small polygon in the middle toward the bottom of the dataset. The normal way to select this would be to use the ArcMap 'Select Features' tool to select it graphically (by drawing a ‘rubber band’ around it). But in my dataset this just causes multiple polygons to be selected.

Here’s a neat way to get around this, using standard ArcView functionality.

1. Open the attribute table and click on the button to show the Selected records only.

2. Now click on the bar to the left of the attribute columns to highlight the row. If you already know your data quite well you can figure out which one you want from the attributes. If you don’t you can simply click through the list until you see the polygon you want highlighted on the map.

3. Right click the record bar next to the highlighted feature. A context menu appears allowing you to perform operations on the highlighted rows.

4. Click Reselect Highlighted to select just the polygon you originally wanted.

Posted by Alexander Allan, GIS Technician, Exprodat.

Tip 16: Polygon Editing

Thu, 12 May 2011

We often need to use editing tools for digitising a new object such as a point, line or polygon feature. These are fairly straightforward to create, but one of the problems with polygons is that we sometimes need to create a new one alongside an existing one. Let us suppose we have a scanned map with two polygons we want to digitise.

If we were to digitise the new polygon (B) using the boundary of the existing one (A), this can sometimes produce areas of overlap from inaccurate digitising of the shared boundary. This could lead to problems where we have unwanted, additional sliver polygons, as shown in the image below.

An easy solution to avoid creating overlapping polygons is to use the Auto-Complete Polygon tool from the Editor Toolbar. To do this follow the simple process below:

In the Editor Toolbar, set Task to ‘Auto-Complete Polygon’ (from Topology Tasks in the dropdown menu) and Target to the polygon layer that you want to add polygons to.

Click once inside the existing polygon then use the Sketch tool to digitise the non-shared boundary (new Polygon B)

Double click back inside the existing polygon (Polygon A) to finish. Alternatively, snap the edit sketch to the edge of the existing polygon.

The newly created polygon should now share the exact boundary of the first polygon.

It’s easy when you know how!

Posted by Mike Phillips, Senior Consultant, Exprodat.

Seismic 2D shot points in ArcGIS 10

Wed, 11 May 2011

(Want to go straight to the download page on ArcGIS.com? No problem)

For a while now Esri’s ArcMap has had the ability to add ‘hatches’ to line layers, enabling you to post labelled measurements along lines (for further information see the ArcGIS Desktop 10 help on linear referencing and dynamic segmentation). The main applications of this in oil and gas are posting shot point labels along 2D seismic navigation lines, or posting information along well paths and pipelines.

However, the interface for adding hatches, while powerful, has never been the easiest for the occasional ArcGIS Desktop user to master. In addition it uses somewhat odd GIS-y language which the average geologist or geophysicist might not understand.

As a result a common deliverable in many of our GIS engagements with oil and gas companies has been a custom ArcGIS Desktop tool for posting seismic shot points along 2D seismic navigation lines.

In ArcGIS 8 and 9 this was done using VBA or compiled code, but in ArcGIS 10, the new add-in capability provides a way to easily share this kind of functionality. We thought it would be a good test of the process of developing ArcGIS Desktop add-ins to create one for 2D line shot point posting.

It turns out that creating an add-in like this was really very straightforward (see the Esri documentation for further information on building add-ins for ArcGIS Desktop), and having tested the tool we decided to make it available to everyone via ArcGIS.com. It’s called the ‘Team-GIS Shot Point Renderer’.

We branded it as a Team-GIS tool as we felt that it was a complimentary product to our commercial ArcGIS Desktop extensions – especially Team-GIS KBridge which creates feature classes with the required M-values when importing 2D line data from SMT’s KINGDOM.

As you can see in the screengrab above, the tool interface is clearly laid out, providing easily configurable minor and major shot point intervals, and options to label shot points as well as the line name itself. In addition, it has some simple controls for configuring scale dependency, so that you can switch off the shot point symbols and labels when you’re zoomed out from the lines.

So, if you’d like to try our free seismic shot point posting tool simply follow this procedure:

Download the Team-GIS Shot Point Renderer add-in from ArcGIS.com.
Install the add-in (see the Esri help on sharing add-ins for further information).
Open ArcMap and select ‘Customize > Customize Mode’ from the menu.
Click the Commands tab and when the dialog has refreshed (which might take a few moments), select ‘Exprodat Tools: Team-GIS’ from the categories list.

Now drag the “Team-GIS Shot Point Renderer” tool from the Commands panel on the right into an appropriate place on your ArcMap tool bar and close the Customize dialog.

That’s it! The Team-GIS Shot Point Renderer add-in is now ready to use. Note that only line layers that have M-values or measures will appear in the tool’s drop-down layer selector. For more information on creating line feature classes with measures see the Esri help topic An overview of creating route feature classes.

By default the tool will appear with just an icon, rather than an icon and the text. To change this open up the ‘Customize’ dialog again and, with the dialog open, right mouse click the toolbar to select ‘Image and Text’, then close the ‘Customize’ dialog.

If you like the tool, please don’t forget to give it a rating in ArcGIS.com - thanks!

Posted by Chris Jepps, Technical Director, Exprodat.

Tip 15: Exporting PDFs

Fri, 15 Apr 2011

Although there are numerous formats for exporting your finished map to, Portable Document Format (PDF) files are possibly the most useful. These files allow users to share high quality cartographic outputs with both a level of security and some flexibility of display. It is also a popular format for delivery to commercial printers, for sharing documents and for the production of in-house reports.

Here is a simple process you can follow to create a PDF export of your map:

From Layout view select Export Data. Select PDF (*.pdf) and then the Format tab. Make sure that the check boxes for Convert Marker Symbols to Polygons and Embed all Document Fonts are ticked. This can be important when sending these files to 3rd parties as objects such as north arrows are ESRI fonts, and if these fonts are not installed at the destination location font substitutions may produce unexpected results.

In the General tab, set the default resolution to either 300 dpi or 600 dpi.

There is also an Advanced tab containing the Layers And Attributes option. Tick the check box for Export Map Georeference Information if you wish to create a GeoPDF (similar to a GeoTIFF) with coordinate details retained in the export. If you do not want the layers in your ArcMap table of contents to be created as separate layers in the exported PDF, choose the None option.

If you want your PDF to contain layers for most of the ArcMap layers, page elements, and other elements of your map, select the Export PDF Layers Only option. If you want to include both features and attributes in the PDF, select the Export PDF Layers and Attributes option.

Note that exporting attributes to PDF can result in performance problems in compatible PDF viewers. If possible, limit exported fields to one layer per map. To suppress field export for a particular layer, turn off field visibility in the Layer Properties dialog box.

To test your result, load the new PDF (GeoPDF) into Adobe Reader and zoom in to see if the map resolution has been retained and is detailed enough for your needs. From the Adobe Reader menu select Tools > Customize Toolbars to open More Tools. Then, in the Analysis Toolbar group tick the check box next to Geospatial Location Tool.

Then from the menu, select Tools > Analysis > Geospatial Location Tool

Moving the cursor around the map displays geospatial location as coordinates, in the bottom right of the map.

Check back with this site soon for more tips that we think GIS users in the oil and gas industry will find interesting.

Posted by Mike Phillips, Senior Consultant, Exprodat.

Extruding data in ArcGIS Explorer 1500

Mon, 11 Apr 2011

In a blog last year we posted an ArcGIS Explorer 1200 add-in that allowed you to create 3D KML representations of feature layers.

We have now updated the add-in for ArcGIS Explorer 1500 and posted it for download @ Esri's ArcGIS.com site.

For further information see the following resources:

3D Extruder add-in User Guide for ArcGIS Explorer 1500
Ross Smail's original blog post on the 3D Extruder for ArcGIS Explorer 1200

The fields selected can all be the same, or can be different. This, combined with the ability to control the colour of the output objects using another numeric attribute field, enables you to symbolise your data on the basis of up to 3 different attributes.

As an example, an oil well could be represented by a column in which daily production controls the column height, total lifetime production controls the column width and the depth of the well controls the colour, ramping from blue to red, from shallow to deep.

Enjoy!

Posted by Chris Jepps, Exprodat.

Tip 14: Exporting a Map with Geospatial Location

Tue, 01 Mar 2011

We spend a lot of time working on our GIS analysis and then creating maps of our results. Eventually we need to do something with our map: present it; get it into a document; send it to another user.

In ArcGIS Desktop (ArcMap) we can make a simple map output by using the menu option Edit > Copy Map to Clipboard, and then pasting the clipboard contents into another software package, such as PowerPoint. This is a great way to provide a quick snapshot of our current map area.

For making a better quality output of the map, we need to export it. ArcMap can export data to a variety of formats including EPS, EMF, JPEG, TIFF, PDF etc. The selected format should be determined by how the map is to be used outside of ArcGIS Desktop.

The export option is available from the menu item File > Export Map.

The level of quality can be improved by changing resolutions, so it is worth experimenting by producing images at different resolutions (measured in dpi, or dots per inch).

Of these options, image formats (e.g. JPEG and TIFF) are particularly useful for embedding into Microsoft Office products such as PowerPoint and Word, as well as most other documentation and presentation packages.

However, as with all the image export options, the exported file remains in a ‘dumb’ format without any geolocation information.

GeoTIFF files

A GeoTIFF, as the name suggests, is a TIFF containing additional geospatial information. This is a format that can imported directly into many E&P packages.

To create a GeoTIFF ensure that you are in Display view. Choose File > Export Map and select the TIFF (*.tif) option. Select the Format tab at the lower left of the dialog and check the Write GeoTIFF Tags check box.

Now switch to the General tab and tick the Write World File check box.

Click 'Save' to export the file as a GeoTIFF. Again, make sure you choose an appropriate image resolution.

Note that you can also export a map to PDF, and this will be a topic of a future blog tip.

Posted by Mike Phillips, Senior Consultant, Exprodat.

Tip 13: Hyperlinking to Document Pages

Wed, 02 Feb 2011

Hyperlinking allows users to establish a connection from one information source to another with just a mouse click. This can be very useful in presentations, and when applied to the GIS environment many forms of documentation benefit from a spatial linkage, e.g. in ArcGIS Desktop you can provide additional information about the features on the map via hyperlinked content.

Commonly, hyperlinks are used either via the Identify tool (a Dynamic, or Simple hyperlink), or by adding them to an attribute table (a Field-based, or Attribute hyperlink).

Dynamic hyperlinks take more time to set up, though have the advantage of being transferable between ArcGIS projects and they allow for greater organisation and management of hyperlinks. If multiple pages from the same document are required, the path can simply be copied and pasted to each record in the field, and the page number adjusted accordingly.

Using the Dynamic method provides a further advantage as it’s possible to link directly to a specific page in a document such as a PDF. To do this, a parameter is added to the address of the document to open the document.

Method

1. First off you need to create a field containing the PDF hyperlink. Lets assume you have a PDF document called A1.pdf, in the folder C:\Data\prospect_reports\pdf. You need to create a TEXT field in the attribute table (e.g. called Link) that stores the hyperlink value for the PDF, i.e. C:\Data\prospect_reports\pdf\A1.pdf

2. You now append arguments to the document name in order to force the document to open at a particular page, such as page 2. First you need to add a delimiter using the symbols ?/ and then you specify the parameter “Page=2=OpenActions”.

The full document path should reads as:
C:\Data\prospect_reports\pdf\A1.pdf ?/ “Page=2=OpenActions”

Note that there needs to be a space either side of the ?/ delimiter.

3. You then need to tell the layer which field to use as the source for the hyperlink. In ArcMap open the Layer Properties dialog for a layer in your table of contents. Open the tab called ‘Display’.

4. Check the box for Support Hyperlinks using field: and select the name of the field containing the Hyperlinks, i.e. ‘Link’.

Now you are ready to check the hyperlink. With the Hyperlink tool selected, hold the cursor over the map feature and ArcMap will display the path to the document and page. Simply click to open the document at the defined page.

This is a fast, simple and effective way to link features in your ArcMap session to particular pages in documents.

Posted by Mike Phillips, Senior Consultant, Exprodat.

Automating your workflows with Python

Wed, 08 Dec 2010

In today’s E&P industry the maxim “time is money” could never be more true. How much time is spent running the same process, the same workflow, clicking the same mouse buttons, tapping the same keys?

Using GIS we have shortened workflows that take many hours into mere minutes, or from months into days. GIS tools may never be able to replace the ZMaps or Petrels but in a first pass review of a new area GIS tools are well positioned to be the first analysis software to use, as they can consume many different types of data and allow you to run many types of analysis.

So what’s Python then? Yes a strange name but potentially critical to the success of advanced use of GIS in the E&P industry. Python is a scripting language – don’t run yet! Anyone can start scripting and the amount of time it could save you will easily compensate for the investment.

OK here’s some background behind Python. Python was named after Monty Python’s Flying Circus by a Dutch man Guido van Rossum. It was designed as an easy to use and learn scripting language, especially for people who have never opened a script in their lives. There’s even a design philosophy called “The Zen of Python”. Python is open source which means that it is developed by the community, it’s free, and that lots of enthusiasts can build modules and expand its capabilities.

Python and ArcGIS

So what’s this got to do with GIS? Well Python is integrated into ArcGIS as a scripting platform allowing you to expand on out-of-the-box functionality to fully automated workflows. You can also easily put a simple dialog on the workflow for passing in parameters if you wish. If you have a workflow that is repetitive then Python will repeat it for you. If you need to do some number crunching with many GRIDs then Python can do this for you. For example, recently I developed a workflow to create Area Depth Pairs which I could run on as many extents as I liked in a single process. There are many possibilities and opportunities for saving time and getting more effective results by using Python in ArcGIS.

Esri have opened up all of the tools in ArcToolbox to be accessible via Python. In ArcGIS 10 access to layers and map functionality has been added to this. For example, I can write a script that will bulk save layers in a map to layer files with a single click.

With Python there are many modules that allow you to expand your workflows. For example, NumPy is a statistical module which has many mathematical equations. GDAL allows interaction with many formats of raster. There are many other modules that can be downloaded. ArcGIS Desktop also includes some scripts that can be found in ArcToolbox, for example Batch Project. Scripts in ArcToolbox look like the image below and you can review the code by right mouse clicking on the tool in ArcToolbox and selecting Edit.

Python is not the complete answer to everything; there are situations where there is a need for development in .NET ArcObjects. But for automating workflows Python provides a great starting point by being an open platform and easy to use.

When to use Python

I’d propose a very simple model to analyse the worth of modelling workflows from other software into GIS in terms of would it benefit a business in saving time or money. I ask some simple questions, such as:

Is the workflow spatial?
What are the inputs (can we get them into GIS format)?
What are the desired outputs (can we get them back into the desired format)?
If the workflow was put in GIS would it save time for the users?
Can the workflow be mostly automated?
Will it save money e.g. other software license costs in comparison to GIS?

If the answer is yes to most of these then it could be Python time! At Exprodat we have developers and consultants who can analyse your workflows and redevelop them if you don’t want to learn Python yourself. We can even help you develop your own scripts or assess if .NET ArcObjects would provide a better solution.

Getting Started with Python

If you would like to get started in Python then there are some good starting tutorials online, two example websites would by Python.org or Dive into Python. You could also attend Esri Python courses; more information can be found at your local Esri website.

Ready to start scripting? ArcGIS Desktop installation comes with a free application called PythonWin where you can start writing Python scripts in (see ArcGIS Desktop's installation notes for more details), and also the embedded Python panel in ArcGIS 10. Alternatively you could use a commercial application called Wing IDE, which has additional functionality.

Posted by Rob Clark, GIS Consultant, Exprodat.

Tip 12: Coordinates and ArcGIS

Thu, 04 Nov 2010

Geographic data is comprised of geographic features – such as wells, fields or licence areas - located by coordinates, referenced to the earth by a coordinate reference system (CRS). The management of geographic data is reliant upon understanding CRSs and successful management of the data’s CRS properties.

The potential impact on oil and gas businesses caused by CRS mismanagement and inaccuracies can be high:

Disputes over licence boundaries as definitions are specified as coordinates
Inaccurate risk assessments in relation to drilling operations
Potential damage to existing infrastructure
Difficulties integrating geographic data with other E&P datasets.

The Esri ArcGIS product suite, in common with most GIS systems, can visualise and manage geographic data specified by coordinates referenced to the earth in different CRSs. The desktop mapping component of ArcGIS Desktop, ArcMap, can correctly align layers of geographic data referenced and stored in different CRSs within the same data frame. This functionality is often referred to as “projecting on-the-fly” and ArcMap undertakes coordinate operations to align layers of geographic data without changing the underlying data itself. To facilitate this, each geographic dataset in ArcGIS has a CRS property indicating the CRS in which the coordinates are referenced to the earth (or “unknown” if the CRS is either not known or has not been set).

The dialog above is from ArcCatalog and displays the CRS property of a feature class. In this case, the CRS property indicates the coordinates of the feature class are referenced in European Datum 1950.

Exporting data for use in other technical systems

Converting a dataset’s feature coordinates to a different CRS is a common requirement of a GIS. The data can then be exported and used in other technical systems that are only capable of displaying data referenced in the same CRS.

As can be seen above, the Feature Class Properties Dialog allows the CRS property of the data to be changed using the Select, Import, New, Modify and Clear buttons. So does this convert feature coordinates of the data to a different CRS?

Well, no! It only updates the CRS property of the dataset - it does not alter the actual values of feature coordinates. As identical coordinate values referenced in different CRS do not refer to identical locations on (or under) the earth, the outcome is ArcGIS will incorrectly interpret the CRS and the data will be misaligned. Effectively, the features will be incorrectly located.

The magnitude of the misalignment depends on the CRSs and the region involved. For example, with two geographic CRSs, WGS 1984 and European Datum 1950, coordinates in the region of the Brent oil field in the North Sea are misaligned by ~110m – a significant amount if the GIS is the definitive source of truth for subsea infrastructure.

With projected CRSs, resetting the CRS property from ED50 / UTM zone 31N to ED50 / UTM zone 32N can lead to gross and significant errors. The location of the Brent oil field is found around 320 km away on the Norwegian mainland.

To make things worse, changing the CRS property in this way does not leave an audit trail in the dataset’s metadata record, making it difficult for future users to diagnose and trace the positioning errors.

Changing the CRS of a dataset’s coordinates

There are two ways to convert feature coordinates to a different CRS using ArcGIS. Both methods create a new dataset and do not change the original dataset.

1. ArcMap’s Export Data functionality.

First, the CRS of the data frame should be set to the target CRS, then the data exported using the same CRS as the data frame. If a coordinate transformation is required, this must be set in the data frame prior to exporting the data, otherwise the exported data may be incorrectly located.

2. ArcToolbox’s Project (for vector data) or Project Raster (for raster data) tools.

Enter the input dataset, output dataset, target CRS and, if required, a coordinate transformation. The advantage of using this method is the tool automatically detects whether a coordinate transformation is required. It also supports concatenated coordinate transformations (transformations requiring more than one step) and the tool can also be embedded within automated workflow models or scripts. Raster datasets require additional parameters – such as the resampling method – but the overall approach is the same.

The Project tool and Project Raster tool can be found in the Projections and Transformations toolset in the Data Management toolbox.

Summary

It is vitally important to understand the difference between changing the CRS property of a geographic dataset and converting the feature coordinates of a geographic dataset to a different CRS. Confusing them can lead to incorrectly located data and potentially some gross errors in the position of the features in your geographic data.

Posted by Ian Milligan, Senior Consultant, Exprodat.

Checking Residuals Bias in Geological Mapping

Tue, 26 Oct 2010

In my last blog post I discussed the case for mapping the residuals in order to help us to understand the quality of the output of the interpolation of a continuous variable.

In there I emphasized the importance of looking at the spatial distribution of the signed residuals to identify whether their spatial distribution may be the result of a random process or a pattern can be identified.

The reason why I am interested in looking at the distribution of residuals is very simple. For the models we have generated by interpolation to be trusted I want their residuals to have two key characteristics:

To show a spatially random distribution, meaning that no spatial pattern of clusters can be recognised. We want overpredictions and underpredictions to reflect the distribution of a random process as much as possible.
To be normally distributed, or as close to a normal distribution as possible. This implies that their mean (average) value is, or is close to, zero.

When the two conditions as above are met, I can reasonably trust that my model is unaffected by the presence of bias, i.e. it does not systematically overpredict or underpredict the values of the variable we are modelling.

Using the interpolation algorithms available in Spatial Analyst one can derive the signed residuals from any model generated by interpolation. Do this by using the “Extract Values to Points” tool available in ArcToolbox under ‘Spatial Analyst Tools > Extraction’.

Alternatively, if the Geostatistical Analyst extension is used to generate models, use the “GA Layers to Points” tool in ArcToolbox under ‘Geostatistical Analyst Tools > Working with Geostatistical Layers’. Note that this extension automatically provides the users with an estimation of the quality of the output by means of cross-validation and, in the cases of kriging and cokriging, with maps of error values.

Deriving residuals for my model

An example of residuals for a continuous variable is shown in Figure 1. I have derived these residuals by simply using the point dataset I have used to generate my model and the model itself as input to the “Extract Values to Points” tool. In so doing I have derived, for each location for which my variable has been measured, the corresponding predicted value. I have then added a new field called Residuals to my table and have calculated this field by a simple mathematical operation (Residuals = Predicted – Observed).

Figure 1: derived signed residuals.

Once I have derived the residuals I can map their signed value. I have simply symbolised the errors based on their sign. In Figure 2 I have used blue dots to highlight underpredictions (negative errors) and red dots to highlight overpredictions (positive errors).

Figure 2: symbolising signed residuals

It is not straightforward to figure out whether a pattern exists in the distribution of the signed errors just by looking at the map. I am therefore in need of a more “robust” approach to carry out the analysis I have in mind.

Using ArcGIS to check residuals bias

ArcGIS provides a number of tools for investigating spatial relationships and assumptions behind spatial modelling. I’m going to focus on a simple tool I can use to gain a quick insight on the spatial distribution of my errors. The tool is called Spatial Correlation (Moran I) and is available in ArcToolbox under ‘Spatial Statistics Tools > Analyzing Patterns’ (Figure 3). Note that this toolbox is available with any level of license and does not require any additional extension to ArcMap.

Figure 3: the Spatial Autocorrelation tool in ArcToolbox

The Spatial Autocorrelation computes the degree of heterogeneity among the values of our variable. Local autocorrelation is computed by assigning a weight to neighbouring residuals based on their relative location with respect to each sampled location. In so doing the tool tests whether statistically significant clusters exist in the values of a variable (in my case the values of my model’s residuals).

If that is the case, I’d expect errors to show a pattern in their distribution, and the randomness requirement discussed earlier to not be met, with my model systematically underpredicting or overpredicting values in some particular areas. In these, I would expect my model to seriously misrepresent the reality.

Once I have launched the Spatial Autocorrelation tool in ArcGIS I’ll be asked to specify the layer and variable I want to investigate (see Figure 4).

Figure 4: the Spatial Autocorrelation dialog in ArcGIS.

I select the optional “Generate report” box - this generates a summary of the main statistical parameters of the autocorrelation which I’m going to use for my analysis.

Various options are available to describe the variable’s spatial relationship, depending on the type and amount of spatial dependency expected in the data.

Tip: If you are dealing with continuous variables and feel uncomfortable with choosing one choice or the other, use the inverse distance or the inverse distance squared (if you expect the dependency to level off fairly quickly with the distance).

If data has been sampled preferentially and is clustered around some specific areas, I would suggest using the “row” standardisation option, which takes into account the presence of clusters when assigning weights.

I would also specify a cut-off distance if I wanted samples located beyond a certain distance not to be included in the correlation analysis. In my example (Figure 4) I have set the Distance Band option to zero because I did not want a cut-off distance to be used in my analysis.

Making sense of the results

Once I have run the tool, the results are summarised in a report as seen in Figure 5 below. A number of statistical parameters are calculated; among these the ones which are relevant for my analysis are the z-score, p-value and the Moran’s index.

The first two provide an understanding on whether the hypothesis of our residuals to be randomly distributed is statistically valid or not. The z-score measures the spread of the distribution around its mean expressed in standard deviation units. This index tells me whether the population of our residuals is close or far from the ideal normal distribution. Classical statistics tells me that in a normally distributed population 68% of its values fall within plus or minus one standard deviation from the mean and 95% within plus or minus 1.96 standard deviations.

Figure 5: report summarising the results of the spatial autocorrelation analysis

The p-value is a measure of the probability of the distribution being the result of a random process. The p-value is usually considered in comparison with a user-defined level of confidence, often set to 0.05 (confidence level 95%). If my p-value is smaller or equal to the set level of confidence, the likelihood of my residuals to be the result of a random process (for instance when z-score value falls within one standard deviation from the mean) is very low and I would expect residuals to show a pattern in the spatial distribution of their values.

If z-score and p-value indicate that there is the likelihood of a pattern in the spatial distribution of my residuals, the Moran’s Index provides an indication of the nature of this pattern. A positive value indicates a clustered pattern, while a negative value indicates a dispersed pattern. In both cases these patterns cannot be regarded to as the results of a random process.

Table 1: summary of spatial autocorrelation resuts

Note that in Table 1 I have assumed two standard deviations from the mean to be a suitable choice for my analysis (z-score cut-off +/- 1.96).
I have also assumed that a level of confidence of 95% is sufficient for the scope of my modelling (p-value cut-off 0.05).

For the example I have used here and the scope of my analysis I would assume that I would be happy with a z-score value within two standard deviations from the mean (+/- 1.96) and a statistical significance of 95% (p-value 0.05).

As shown in Figure 5 my z-score is +0.16 (falls within two standard deviations as I required) and the p-value is 0.88 (higher than the significant value’s threshold of 0.05). In this case I can be quite confident that the distribution of my residuals indicates a random process. I don’t even need to consider the value of the Moran’s Index, as no significant patterns (clustered or dispersed) can be identified.

If, on the other hand, the result of my analysis had shown z-score values falling outside the desired significance and p-values smaller than the required statistical significance, I would have expected a pattern to be identified in the distribution of my residuals. In such a case the sign of the Moran’s Index would have identified whether the pattern would have been a clustered or dispersed one.

In this last case I would probably need to reconsider my modelling process, by choosing a different algorithm or changing the parameters controlling the interpolation (search strategy, directional influences, etc). I could then generate the residuals for the new model(s) I had derived, and run the Spatial Autocorrelation tool again to test that the basic assumptions of (a) spatially unrelated residuals which are (b) normally distributed are being met.

Posted by Paola Peroni, Senior GIS Consultant, Exprodat.

Promoting GIS in your Workplace

Tue, 19 Oct 2010

Are you working in an organisation that you feel could really benefit from GIS, but don’t know how to go about selling it to the right people? Here are a few ways that I’ve found useful for promoting GIS to the decision makers:

KISS - The key to selling any new concept to a busy business exec is to Keep It Simple, Stupid. You might only get one shot at this, so start with a teaser. Prepare a 5 min presentation with plenty of additional material to embellish if necessary. You can always reschedule another meeting in the future, and tailor the content in response to your first. Your main aim at this stage is to get approval for further research.
Use consumer reference points - Your manager might not know it, but he’s probably more GIS savvy than he realises. Has he ever used Google Earth? Satnav? Mobile phones? Over the last 5 years we have seen a proliferation of GIS related applications in the domestic market. From hand held devices such as GPS, mobile phones, and satnavs to desktop and online applications such as Google Earth, Twitter, or even Facebook; make the most of any examples that your audience might already be familiar with.
Be industry relevant - Make your examples as relevant to your industry as possible. Identify the problems you see the company facing and explain how you think GIS can assist. If you’re not an expert, don’t panic, and don’t pretend to be one! There is a wealth of resources on the internet to research; all you need is a bit of free time.
Bring everything back to the bottom line - It’s all about the bottom line! Don’t just focus on how much a system might cost to implement – balance this by illustrating the potential return on investment, e.g. through efficiency savings, improved decision making and better risk management. Esri’s own Business Benefits of GIS site is a great resource, although very detailed. You might also consider using a pre-existing strategy approach, e.g. you own organisation’s, or Exprodat’s GIS Maturity approach.
Generate buy-in before doing demos – Demoing a system that doesn’t solve a real business need could do more harm than good and make you look technology-led. So, don’t start building flashy demos until you’re starting to get management buy-in. As you discuss the potential of GIS in your company you will probably identify priority areas where GIS can provide clear business benefits. Holding back on the technology now will help you build relevant and compelling demos later.
Use simple tools - Having identified which area to focus your demo, you now need to select the right technology. Make your system simple enough for decision makers who don't have time to learn complex technical applications. There are many products out there aimed specifically at entry-level users. Products like ArcGIS Server, MapIT or ArcGIS Explorer (a great free entry-level alternative) provide customisable GIS interfaces to make GIS specifically task orientated. Use these tools to demonstrate that GIS does not have to be exclusive to the specialists.

These are just a few starters to get you thinking - if you've been through the experience of implementing (or trying to implement) GIS in a business or organisation, please share your experiences and hints/tips here.

Posted by Tom Stephenson, GIS Technician, Exprodat.

Complex Symbols and Cartographic Representations

Fri, 03 Sep 2010

I want to create a complex custom symbol for use with area fills on a map. In ArcMap this can be done either with a multiple stacked marker symbol which is time consuming and difficult to construct, or using a small bitmap and importing that as a picture symbol. The problem with picture symbols is that they can appear blocky, especially when plotting.

After some investigation I found that there is a better way – to create a cartographic representation. Two things make this ideal: there is a convenient and easy editing interface available (the Marker Symbol Creator) for creating complex symbols and the flexibility of the representation rules can be used for effectively displaying it.

Note an ArcEditor or ArcInfo licence is needed to create cartographic representations, but they can be used with an ArcView licence. They do not replace layer files or traditional style files, but should be considered to be an addition to them.

Creating a new representation using the Marker Symbol Creator

First create a feature geodatabase (GDB) and create or add a dataset. You'll need some example points, lines and polygon layers for this. In ArcMap, load the dataset then right click on it and select “Convert Symbology to Representation…”

Several options are provided within the “Convert Symbology to Representation” dialogue but just keep the defaults. This conversion creates a duplicate of the layer in the TOC.

When the “Layer Properties” dialogue is opened for the representation, an additional option exists on the “Symbology” tab – Representations. This is where the rule is applied to the representation. To create a new marker pattern just click on the “Add new marker layer” button. This populates the central panel on the dialogue. Clicking on the green point symbol at the top opens the “Representation Marker Selector”.

Clicking on the “Properties” button opens the “Marker Editor” interface.This allows full editing of the symbol just like a graphics package. Delete the existing spot and create your own symbol using the available tools of the interface. Within five minutes you can create your own custom symbol of an oil derrick:

Name this symbol ‘Oil Derrick’ instead of ‘Rule 1’, then make a second symbol called ‘Nodding Donkey’. To do this click on the “Create New Rule” button and follow the same process. The nodding donkey symbol is a bit more complex, but should only take 10 minutes to construct.

Save these as representation symbols by clicking on the “Save” button from the “Representation Marker Selector” dialogue. Note these are saved within the ‘Representation Rules’ part of the user style file as can be seen in the “Style Manager”. They can be copied within the “Style Manager” to a standard company symbology style file as ‘Representation Rules’ for use with other projects.

Now you should have two symbols that can be used as a “pattern” on a polygon.

Using a marker symbol on a coloured area fill

After creating the new complex marker symbol you can use it in an area fill pattern. First you need to convert your polygon to a cartographic representation.

Open up the “Layer Properties” dialogue for the new representation and edit the ‘Rule 1’ with the “Add New Marker Layer” button. Note you could have imported a symbol from an existing style at this stage.

Next click on the black marker symbol to open the “Representation Marker Selector” (see the red highlight below):

Your new symbols are now available at the bottom of the list. When selected you can then apply some changes to the size, X and Y steps and set the ‘Inside Polygon’ marker placement, by clicking on the drop-down arrow to the right of the ‘Polygon centre’ text on the panel (see the green highlight above).

The ‘Clipping’ is selected as ‘No markers touch boundary’. Note symbols can also be rotated. These symbols line up vertically and horizontally as a default. To stagger the markers check the ‘Shift odd rows’ box.

Can more than one marker symbol be used?

Yes - To do this simply add a second marker symbol using from the “Add New Marker Layer” button. The default will put both symbols on top of each other, so use the X and Y offset on one symbol to separate them and also double the X and Y step for both symbols:

So now you have two high quality custom vector based symbols that can be used as point symbols, or along a line, or distributed within an area fill. Use this technique to givie a more professional look to your cartographic quality for those important maps!

Gavin Adcock, Senior GIS Consultant, Exprodat

Extruding data in ArcGIS Explorer 1200

Wed, 18 Aug 2010

ESRI’s ArcGIS Explorer (AGX) is a free downloadable GIS viewer which allows viewing of a wide variety of spatial data in either 2D or 3D (Globe) display modes.

This blog describes a utility I’ve developed that allows features in AGX feature or package layers to be exported to ‘extruded’ 3D KML (to download it see the bottom of this blog).

The reason I created this utility was that I didn’t like the rasterised representations of our 2D polygon and line vector layers in native AGX. 3D KML is displayed as vector objects and hence looks ‘better’, especially when moving from viewpoint to viewpoint (much nicer for dynamic AGX presentations).

The screengrab below illustrates the differences between a KML representation of polygons and points on the left, and the default AGX representation of the same data on the right.

An additional attraction was that I’d created KML exporters before, but these didn’t display the KML output in the same application – you had to launch Google Earth or AGX. AGX provided the application platform to view a 2D layer, export it to 3D KML then view the result, enabling the process to be iterated in one application, making it potentially far less painful to create an acceptable result.

At this point, I was going to waffle on about ‘Symbolisation of data for presentation’, using phrases and words such as ‘in a static 2D medium’, ‘discovering your data’, ‘whole new realm’, ‘interactive’ and ‘involved’, in an effort to justify the effort I’ve put in, but basically it's just fun making 3D things, and then flying around them.

The screengrab below illustrates Californian cities represented by scaled COLLADA models, the height of the models being controlled by the population of the city. Note that the attributes of the input features are also exported, allowing you to click on a feature to view them in a pop-up, as shown.

Anyway, I built the main part of the utility last year. However, having reviewed it, I decided that the output KML really needed to incorporate a legend that described what the styling of the exported features actually represented. Without a legend, the representation of the data within the KML would be at best difficult to decipher, at worst meaningless to anyone other than the creator. A few projects have intervened between then and now, and I’ve only just completed and tested the legend generation routines.

The utility allows you to select numeric attribute fields which will control the height and colour of the output objects. For point and line datasets, you can also control the width of the output objects using another numeric attribute field. For COLLADA models you can control the insertion height and the scale of the model (all dimensions are currently equally scaled). The fields selected can all be the same, or can be different. This, combined with the ability to control the colour of the output objects using another numeric attribute field, enables you to symbolise your data on the basis of up to 3 different attributes. As an example, an oil well could be represented by a column in which daily production controls the column height, total lifetime production controls the column width and the depth of the well controls the colour, ramping from blue to red, from shallow to deep.

My legend display is provided by a three entry legend (low/mid/high) for each controlled parameter – e.g. if you’re controlling height and width by different attribute values, you get two three-entry legend elements. They’re not particularly pretty, but they hopefully serve their purpose.

There are a lot of things that aren’t ideal about the utility, and some bugs, but I hope you find it useful. If you have any suggestions for improvements, please do contact us using the link below. I have included some ideas of my own at the back of the documentation PDF.

[To Download the ArcGIS Explorer data extrusion add-in please see our updated blog post for v1500].

Posted by Ross Smail, Head of R&D, Exprodat.

Measuring the Quality of Predictions: 3

Wed, 14 Jul 2010

Assessing the Quality of the Modelling of Continuous Variables in the 2D Domain – A Deterministic Approach

See Part 1 and Part 2 of the 'Measuring the Quality of Predictions' discussion.

Distribution of Uncertainty

Unfortunately, limiting the analysis of uncertainty to the magnitude of the residuals alone is proven to be too limited an approach. Indeed, very useful information can be obtained by looking at the distribution of both predictions and residuals.

A linear correlation analysis is a simple and quick technique which we can use to compare the distribution of observations, predictions and their residuals. By using the correlation concept two indices can be added to our analysis.

The first, called Value Correlation Coefficient (VCC in Table 1), is simply an expression of the linear relationship between predicted and observed values. In this sense we would expect that models showing values of predictions closer to the value of observations to be affected by lower uncertainty than models showing larger discrepancies. The VCC, whose values span from -1 to +1, will therefore provide an indication of how close our predictions are to our observations for the whole of the input point population.

In an ideal world all predictions match the values observed in all the sampled locations and, therefore, the correlation coefficient would be equal to +1. This is obviously not going to happen in the real world so we would like our models to have VCC as close to 1 as possible.
For instance, let’s compare the values of VCC for two of the deterministic models we have derived by using our input point dataset. In Figure 5 below, plots A and C show the distribution of observations (dependent variable) and predictions (independent variable) for two models derived using ArcGIS Spatial Analyst (A = Derived using 'Natural Neighbor' algorithm, C = Derived using 'Spline with Barriers' algorithm).

The scatterplots shown in Figure 5 are powerful diagnostic tools and can be used to help gain an understanding how a model performs. In our case, note how much more dispersed the cloud of points is for the 'Natural Neighbor' than for the 'Spline with Barriers' model. The higher correlation between predictions and observations for the latter is reflected by the higher VCC coefficient (0.71) compared to the former model (0.4). Note also how both models struggle to correctly predict values at the higher extreme (points more scattered), and how the 'Natural Neighbor' model also underperforms in predicting values at the lower extreme of the distribution.

The last index we want to consider is also a linear correlation coefficient. The Error Correlation Coefficient (ECC) is a measure of the correlation between the absolute value of the residuals and the magnitude of the observations. This index provides an idea of the bias in the residuals and, therefore, how far the population of residuals is from the ideal, symmetric distribution centred on a zero mean and with a minimum spread. These elements are reflected by the ECC being as small as possible, confirming the independence of the errors of predictions from the true values of the observations.

In the example we are considering here note how the residuals are much more dispersed for 'Spline with Barriers' (Figure 5 D) than for 'Natural Neighbor' (NN) model (Figure 5 B). The latter, in fact, indicates the presence of an inflection or a dip, which may be an indication that a linear model may not be appropriate to describe the spatial behaviour of the variable we are trying to model.

See Table 1 for the ECC values for these two models. You will notice how ECC is higher for NN (ECC = 0.51) than for the 'Spline with Barriers' (ECC = 0.32), indicating the latter as a better performing model.

Figure 5 – Correlation coefficients scatterplots for the NN (A, C - VCC) and the 'Spline with Barriers' (B, D - ECC) models

Adding the Spatial Component of the Uncertainty to the Analysis

The indices discussed so far do not take into account the spatial component of the prediction errors. Residuals can be expressed not only by their values (relative, absolute) but also by the spatial location where these values occur. Within the context of this discussion it could be extremely useful to map the residuals.

The presence of spatial patterns or spatial clusters of particularly high errors is key information which can be gained by simply putting the residuals on a map. In doing so we not only obtain information on the overall performance of any given model, but also would indentify regions where our models provide more reliable results and areas with high uncertainty which may be worth further investigated.

Figure 6 shows the error maps for the 'Natural Neighbor' and the 'Spline with Barriers' models. Residuals are mapped based on the signed value of errors (maps A and B). Map B shows no detectable patterns of underestimations or overestimations, where signed residuals are distributed randomly throughout the study area. Map A shows how the areas where predictions are underestimated (blue dots) and overestimated (red dots) are quite distinct one from another.

The presence of non-random patterns in the spatial distribution of the signed residuals may be used as an indication that the custom parameters which were used to derive the model need to be revised and require better calibration, or as an indication that the structure of the model is unsuitable to describe the behaviour of the continuous variable.

Graduated symbol maps C and D (Figure 6) show the spatial distribution of the absolute value of the errors. Both show that the regions surrounding major faults are characterised by higher discrepancies between observations and predictions. Note that this is also true for the 'Spline with Barriers' algorithm, which can handle faults automatically.

The final scope of the modelling should determine whether the results are acceptable or not. In our case if the scope of the modelling is to obtain a high level of confidence particularly in areas near the source of discontinuity (fault lines, ridges, rivers and other linear features) a more elaborate approach may be needed. This may involve separately modelling distinct regions that are delimited by the fault setting, or using a different interpolation approach altogether, e.g. using geostatistics instead of a deterministic method.

Figure 6 – Mapping the residuals and the absolute values of the residuals for 'Natural Neighbor' (A, C) and 'Spline with Barriers' (B, D) models

Conclusions

In this three-part series of blogs we have discussed a simple and quick approach to evaluate the quality of the predictions derived from the interpolation of a continuous petrophysical variable.

The methodology is well known and provides a basic approach to compare models generated by using different deterministic algorithms and can easily be added as a key task at the end of modelling workflows.

In our experience of the E&P industry we often see that the results of the modelling process are taken without questioning the validity and the quality of the output. We hope that the “practical cut” we have adopted for this discussion will benefit geoscientists who may have felt uncomfortable with complex statistical concepts and may welcome a more basic, yet still scientifically sound, approach.

If you missed the earlier parts of this discussion you can catch up using the following links:

Please do let us know whether you have found this discussion useful by posting comments below. We welcome and value your feedback!

Posted by Paola Peroni, Senior Consultant, Exprodat.

Paola presented elements of this methodology at the ESRI User Conference, Tue Jul 13th, 2010 (room 28 C), in San Diego.

Measuring the Quality of Predictions: 2

Mon, 12 Jul 2010

Assessing the Quality of the Modelling of Continuous Variables in the 2D Domain – A Deterministic Approach

See Part 1 of How can I measure the quality of my predictions?

Estimating Prediction Quality through Validation

Whilst a geostatistical approach to the interpolation provides the user with a direct estimate of the errors of the predictions, this is not the case in many software packages that use deterministic algorithms, at least as a default output of the interpolation process.

Considering that the use of deterministic algorithms is a frequent choice in many practical E&P workflows (generally speaking they require less customisation, therefore making the modelling process less time-consuming), it is quite common that the assessment of errors is often a by-passed, undervalued step.

How then can we gain an idea of how well our models perform and how much trust we can assign to them? How can we combine our “common sense” with a simple but scientific analysis of the modelling output?

One of the easiest approaches we can take to answer to these questions is through a process called Validation. By means of Validation the original sample dataset is divided in two sets of data: the first (called the 'training dataset') is used to optimise the values of the parameters which control the modelling, while the second (called the 'test dataset') is used to validate the predictions. Validation of the predictions is done by simply comparing the value of the variable derived by using the training dataset at the sampled locations of the test dataset.

The number of sampled points within both the training and the test datasets depends primarily on the total number of sampled locations. It goes without saying that our original input dataset needs to contain enough observations not only to provide a stable model, but also for carrying out a sound Validation process.

The number of observations, however, is only one of the elements which are to be considered when splitting between training and test dataset: the other key factor is the relative locations of observations. For instance, if a short-range component is a key feature of the continuous variable we are modelling we want to make sure that a significant number of neighbouring samples are included in the training dataset, to allow this local component to be captured by the model.

As an example consider the sample point population shown in Figure 2, which has been split into a training dataset (yellow points) and a test dataset (blue points).

Figure 2 – Split of a sample population into training and test datasets.

For the particular petrophysical variable we are modelling in this example, our knowledge of the geological setting would tell us that the predominance of a local variation is expected. Therefore the spatial proximity of points within the range of the expected local variation should be the main element considered when splitting the original input dataset into two subsets.

Magnitude of the Uncertainty

Now, let’s assume we have derived various models by using different algorithms and optimising the parameters which control the interpolation process, such as the search neighbourhood, power, and factors controlling smoothing and anistrotropy.

Figure 3 – Predicted values and residuals at the test dataset point locations

The first element we can consider to evaluate the quality of each prediction (model) is the magnitude of the residuals. It is easy to understand how the performance of our predictions can be directly correlated to the magnitude of the residuals, with the most reliable models showing the lowest residuals values.

The overall magnitude of the residuals can be estimated by a simple parameter called Root Mean Squared Error (RMSE) index. Although this parameter may be affected by the presence of bias in the residuals population, it has the great advantage of being easy and quick to derive. It can be used to obtain a first idea of the quality of the predictions, with lower values of RMSE indicating lower magnitude of errors.

As an example consider Table 1 which summarises the RMSE values for four deterministic models derived by using different local interpolator algorithms available in ArcGIS Spatial Analyst: Inverse Distance Weighted (IDW), Natural Neighbors (NN) and Spline (with and without barriers). The training dataset of Figure 2 was used to derive the models, while the test dataset was used to derive the residuals at the measured locations.

Note how the 'Spline (with barriers)' algorithm in this case provides the smallest RMSE, indicating that the overall values of the residuals are smaller for this model than for the others.

Table 1 – Model performance comparison matrix

Two additional indices can be used jointly to obtain additional information on the magnitude of the uncertainty (see Table 1). The Mean Absolute Error (MAE) measures the average absolute difference between observed and predicted values and is simply expressed by the mean of the absolute values of the residuals (e.g. not considering whether the prediction over or underestimates the observations). It provides a simple and quick estimation of the magnitude of the bias of the models and, as a consequence, better performing models are the ones with least prediction bias and, thus, smaller MAE.

It is very useful to evaluate the magnitude of the bias in our models in conjunction with the variance of absolute residuals. The Error Variance (EV) is a measurement of the precision (or lack thereof) in our predictions; a high value in the variance of our residuals indicates a smaller correlation between predictions and observations and, therefore, a poorer performing model. Ideally, therefore, we want to minimise the variance in the population of our residuals.

It is often useful to evaluate the MAE and the EV indices together, as large MAE and relatively large values of EV can be considered an indication of larger discrepancies between predictions and observations. Figure 4 below shows graphs of the magnitude of the three indices presented so far for each of the the four models.

Figure 4 – Comparison of uncertainty magnitude indices for four deterministic models

It can be seen how the 'Spline with barriers' model consistently provides the smallest residuals (RMSE and MAE) coupled with the smallest values in the variance of the residuals.

The comparison of these simple indices indicates that, for the example considered here, the 'Spline with barriers' algorithm is more capable of capturing the spatial characteristics of the petrophysical variable we are modelling than the others considered here.

To be Continued….

This is the second of a three-part blog on the subject of the estimation of uncertainty, if you missed it you should first read Part 1 of "How can I measure the quality of my predictions?"

In part 3 of “How can I measure the quality of my predictions?” I will discuss how the distribution of the residuals and the spatial component of the uncertainty can be brought within the analysis.

Posted by Paola Peroni, Senior Consultant, Exprodat.

Paola will be presenting elements of this methodology at the ESRI User Conference, Tue Jul 13th, 2010 (room 28 C), in San Diego.

Tip 11: ESRI Tips and Shortcuts

Thu, 08 Jul 2010

If you're an ArcGIS Desktop user you might be interested to know that there are a number of keyboard shortcuts available to speed up frequently used operations and save you some mouse mileage:

My personal favourite is F9 - stop drawing!

Posted by Chris Jepps, Technical Director, Exprodat.

Measuring the Quality of Predictions: 1

Wed, 07 Jul 2010

Assessing the Quality of the Modelling of Continuous Variables in the 2D Domain – A Deterministic Approach

Background

Any quick search on Google on the subject of “Assessing the quality of models in the 2D domain” would provide tonnes of results, with a number of them referring to research papers proposing various ways to evaluate the quality of the output of a modelling process. The complexity of the theoretical approach described in these papers, however, is often the element which prevents the practical application of these assessment theories to our every-day workflows.

Evaluating the errors of our modelling effort has, nonetheless, practical advantages. Among them is the understanding of the quality of our models. Interpolation, i.e. the procedure of predicting the value of attributes at unsampled sites from measurements made at point locations (Burrough & McDonnel, 1998), is a typical example of a task in which the quantification of the errors of the predictions provide information not only on the quality of the choices we have made during the modelling process (in particular which algorithm and parameters to use), but also how well our models approximate the characteristics of our continuous variables.

Practical Assessment Methodology

At Exprodat we have often used a simple, practical assessment methodology which can be easily applied as the final, key step to a general interpolation workflow shown below.

The methodology, tested and used widely to provide an understanding of the quality of the prediction of any continuous variable (in space, or time), is focused on a series of simple, easy to calculate parameters which can provide an insight on the performance of models.

Residuals are used as the key elements to evaluate the characteristics of the error population associated with the modelling effort. In particular, we look at two features of the residuals:

their magnitude, both in term of absolute values as well as signed values
their spatial distribution within the study area.

It is clear that in analysing the “fit-for-purpose” of the modelling process we have applied, not only are we interested in understanding how our models perform on average, but also where they provide more reliable results and whether there are patterns within the distribution of errors which can be recognised. The presence of patterns of high values of uncertainty could, for instance, be used to understand the ability of our models to predict values at the extreme of the distribution, an element that can be crucial in modelling workflows where the ability to model very high or very low values is of particular importance.

The Case for Investing Time in Evaluation of the Reliability of our Models

It may be argued that a model is just what it is: one of the possible interpretations of a complex reality, and in many cases we can get away with this. In this sense we may trust our results and consider that they are “reliable enough” for the purpose of our analysis. We do not need to go any further in our investigation.

On the other hand, what if we were able to measure the reliability of our models, so that we could pick the ones which are able to better portray the features of the continuous variables we are modelling? Wouldn’t we want to go that bit further and invest some time in evaluating the uncertainty if this could turn our modelling into informed-decision-making?

To be Continued….

This is the first of a three-part series of blogs on the subject of the “Assessing the Quality of the Modelling of Continuous Variables in the 2D Domain – A Deterministic Approach”.

In part 2 I will discuss some simple parameters that can be used to assess the quality of models generated from point datasets by using a deterministic approach to interpolation.

Posted by Paola Peroni, Senior Consultant, Exprodat.

Paola will be presenting elements of this methodology at the ESRI User Conference, Tue Jul 13th, 2010 (room 28 C), in San Diego.

Tip 10: ESRI Symbols Fonts

Mon, 05 Jul 2010

Recently someone asked me how to use ESRI well symbols for bullets in MS Word documents and MS PowerPoint Presentations. Initially my first thought was to make a screen grab of the well symbol using Paint or Snagit, a useful utility software. This would provide a small image of the symbol that would be ideal. But surely there must be a more elegant way of using the symbol?

Yes there is – the ESRI Well Symbols are “Marker Symbols” which means they are stored as a character set in the same way as a font set such as ‘Arial’, ‘Times Roman’ or ‘Wingdings’.

In ArcMap the name of the symbol set can be found by using the Styles Manager – ‘Tools > Styles > Style Manager…’. Next, click on the Styles button and select the ‘Petroleum’ or ‘Petroleum UK’ style. To display the individual well symbols expand the ‘Petroleum.style’ and click on the ‘Marker Symbols’ folder.

Let's assume that my required symbol is “Oil and gas well”. If I scroll to this entry in the list and double click it the ‘Symbol Property Editor’ dialog opens. From the dialog I can see that the font for this symbol is called ‘ESRI Oil, Gas, & Water’, and its 'Unicode' value. This information will allow me to add the character into other applications, the same way as, say, a Wingding Smiley Face symbol in a MS Word document.

To add my symbol into MS Word I can simply insert it by navigating to the menu option ‘Insert > Symbol’. Then I select the ‘ESRI Oil, Gas, & Water’ font and the required symbol in the ‘Symbol’ dialog. Note that if I can't see the symbol in the list I can simply type the number seen in the 'Unicode' earlier into the 'ASCII (decimal)' character code field.

When I clck 'Insert' the ESRI well symbol is inserted into the MS Word document at my current cursor position, as a nice sharp symbol.

With the symbol now added in MS Word it can be used o create custom bullets in the document. By selecting ‘Format > Bullets and Numbering…’ and then clicking on the ‘Customize’ button in the ‘Bullets and Numbering’ dialog. This opens the ‘Customize Bullet List’ dialog where the ‘ESRI Oil, Gas, & Water’ font can be selected from the ‘Font…’ button and the symbol can be selected from the ‘Character…’ button.

Now I have a nifty well symbol as a custom bullet both in MS Word and MS PowerPoint for my presentation instead of the usual black dots! Note that in MS PowerPoint the steps are pretty much the same except for selecting ‘Format > Bullets and Numbering…’ from the menu and then clicking on the ‘Customise’ button in the ‘Bullets and Numbering’ dialog.

Posted by Gavin Adcock, Consultant, Exprodat.

Crowdsourcing and ArcGIS Server 10

Thu, 06 May 2010

With the imminent arrival of ArcGIS Server 10 and its template-based online spatial data editing it looks as though map-based crowdsourcing (which has been around for a while via Google Earth etc.) will soon be a reality for the ESRI community.

ESRI themselves have quite a neat example of this available on their website, monitoring the BP/Deepwater Horizon oil spill in the Gulf of Mexico. Its simple editing interface allows the public to post up links, photos, videos and notes, in an attempt to increase everyone's awareness of activities related to this tragic event.

This started me wondering how this type of online editing might be applied to the upstream domain, and specifically within oil exploration, as a type of 'private crowdsourcing' (forgive the neologism!) - i.e. internally within an organisation's firewall.

A few examples where I think it might prove useful are:

Data integration in play fairway mapping?
Use as a geological analogues database?
Competitor acreage tracking/scout database?
Project team document management solution?
Virtual team collaboration and data compilation?

It'll be interesting to see how this area develops after ArcGIS 10 is released, and whether the concept of 'private crowdsourcing' will take off, potentially as an alternative to traditional document management initiatives within smaller distributed teams or organisations.

Posted by Chris Jepps, Technical Director, Exprodat.

Tip 9: Relative Paths

Wed, 05 May 2010

When you start up a new ArcMap document, you almost immediately and probably unknowingly lock yourself into using either relative or full folder paths:

A relative folder path specifies the location of files brought into the project in relation to where you have saved your ArcMap document for the project.
A full folder path specifies the location of files brought into the project using the complete path to the data, including the drive information.

So the first thing to do is to make sure that you have created a folder structure for your data and you have a folder named something like “mxd” to store your ArcMap documents in.

An example simple project folder structure might be:

C:\project\mxd\my_project.mxd
C:\project\geology\geology.fgdb\lower_cretaceous
C:\project\layers\geology\Lower Cretaceous Play.lyr

The relative path from the mxd to the dataset is ..\geology\geology.fgdb\lower_cretaceous and this works in a similar fashion for other data sources. The “..” means go up one folder level from the mxd file and read the rest of the file reference from there.

You should be aware though that relative paths work only across the same drive, so if you are using files stored on a different drive to your ArcMap document they will be specified as full paths even if you have set your project to relative paths.

A full path is what most people are used to. In the example project folder structure above the full path reference to the data is C:\project\geology\geology.fgdb\lower_cretaceous

To set the map document to use relative paths you should select the following option:

File > Document Properties... > Data Source Options…

Clicking the Data Source Options button brings up the dialog allowing you to choose full or relative path names, and also to set the default for all future projects.

Which to Use?

I think there is one simple question that you need to answer (ok nothing is ever that simple – but very often if the answer is definitive, then you know which method to use):

Question: is it likely that during this project or anytime in the immediate future I am going to have to move my mxd file(s) to a new location without moving the rest of the data?

Answer – Yes: You are probably better off using full path names, because on moving the MXDs you will be chaning the relative position of the MXD to the datafiles.
Answer – No: If you are likely to move only the entire project folder structure (including MXDs) then relative path names will save you heaps of work!

Posted by Chris Skelly, Training Manager, Exprodat.

Tip 8: Geoprocessing Masks

Tue, 27 Apr 2010

In GIS terminology a ‘mask’ is a spatial layer that one uses to limit the ‘view’ of a region. For example, if you were looking at the geology of the North Sea region, but only really interested in the marine environment you might employ a ‘land mask’. This would be a polygon that lies on top of your geology blocking the bits on land from view, so that only the marine geology was visible. GIS users have been using masks cartographically like this for as long as GIS has been around.

It’s all getting a bit more sophisticated these days, and there is now something in ArcGIS Desktop called a ‘geoprocessing mask’. A geoprocessing mask specifies a region that will be included in any geoprocessing functionality.

As an example, let’s say that you have a task where you want to calculate the minimum distance from every well to the closest platform. If you were to run this process for the entire North Sea, it might take a while. If you were only interested in the wells within a particular licence block, then wouldn’t it be really cool if you could just do this geoprocessing on your licence block and mask out the rest of the North Sea? Yes? Ok, that’s what a geoprocessing mask does for you.

Mask Hierarchy

The tricky bit is that you can set up three different hierarchical levels of geoprocessing masks. These are:

Tool Mask
Model Mask
Application Mask

BUT one will always take precedence over the others.

If you are a new or casual user of ArcGIS you might not know that 'environment' settings affect geoprocessing functions in a reverse hierarchical manner. In other words, they are the least important of the 3 levels.

So, if you set a geoprocessing mask, for a specific tool, say, the Buffer Tool, it trumps any other geoprocessing mask set in ArcGIS Desktop, such as application 'environment variables'. Conversely, a geoprocessing mask set at the application level is only applied when no other masks exist for the specific Tool being used or for a Model in which the Tool might be used.

The 3 levels of geoprocessing masks are set in the following ways:

Application Settings

Once set, the application (or 'environment') settings apply to all geoprocessing within the ArcMap document except for the Models and/or Tools for which you have set the same environment variable. The environment settings for the ArcMap application are set via:

Tools Menu > Options > Environments… > Raster Analysis Settings > Mask

Model Settings

Once set they apply to all geoprocessing tools used within the model and supersede all application settings, except where the environment settings have been established for a tool within the model. The Mask setting is established for a model using:

Right-click model > Properties > Environments… > Raster Analysis Settings > Mask

Tool Settings

Once set, these tool settings supersede all other environment or model settings. Using the Buffer Tool as an example, the geoprocessing Mask is set via:

ArcToolbox > Proximity > Buffer > Environments… > Raster Analysis Settings > Mask

So there you have it! The concept of a spatial mask is fairly straight forward, but when used in geoprocessing we have to remember that there are three different levels at which it can be set. Additionally, Tool Mask trumps Model Mask trumps Application Mask!

Posted by Chris Skelly, Training Manager, Exprodat.

Behavioural Economics and Innovation

Thu, 08 Apr 2010

I was very interested by a recent blog by David Bamford on the subject of behavioural economics. The bottom line is that buyers of technology are fickle and not altogether objective when it comes to buying new technology, for a variety of very human reasons:

Benefits are only realised sometime in the future
Benefits are uncertain: the product might not work as expected
Benefits are usually qualitative: it’s difficult to enumerate the value and make absolute comparisons between different options
Buyers (over)value the items in their possession more than prospective items
Buyers are loss-averse.

And likewise for the purveyors of technology: their view of the value of their offerings is often very different to their prospective buyers.

As the owner of a small company that develops technology, this all certainly rings true! The initial development to early market acceptance/pay-back phase for new technology can take several years. That's a big investment in time and capital for a small company.

I've also definitely noticed a reduction in 'IT' projects risk tolerance in our clients over the past 5-10 years. It seems to me that many E&P companies are trying to squeeze all the risk out of IT spend decisions and projects. If only they were all as diligent at reducing Exploration risk where the real money is spent, but that's another discussion!

I agree that technology selection and implementation needs to be handled with due diligence and managed properly. But are companies 'throwing the baby out with the bath water' and forgetting how to experiment and innovate?

Some of our best products have come from working closely with clients that are prepared to take a risk on new technology or different approaches. Not everything works, but when it does, the value often far outweighs the downside from the other less successful experiments. These clients are increasingly rare. Most now look for tried & tested solutions, expecting others to have taken the risk out of the decision for them.

In the long run, I feel there's a real danger that the smaller innovators will cease to feed through next generation technologies and ideas, because of this 'perfect storm' of behavioural economics and IT risk aversion in the petroleum sector. There is of course a flipside: The technology business also needs to take a more realistic view of the value of the products they develop, regulate their marketing hyperbole and build confidence in the buyer community.

Posted by Gareth Smith, Managing Director, Exprodat.

Reduce Your Oil Exploration Risk

Wed, 17 Mar 2010

International oil and gas companies are presented with so many exploration opportunities that they cannot all be actively pursued. In order to decide where to commit resources the exploration opportunities need to be analysed and ranked (preferably as quickly as possible), based upon the quality of each opportunity and the risks (geologic, engineering and economic) associated with it.

This blog outlines some simple ways to improve your understanding of the geologic risk associated with your oil and gas opportunities, be they plays, licenses, prospects or producing fields.

Resource Appraisal in Oil Exploration

It has been well documented (see below) that by better understanding the risks associated with each of their plays petroleum companies can reduce technical uncertainty in their exploration efforts:

Resource Appraisal Methods: Choice and Outcome - Miller, AAPG, 1986
Geologic Risking Guide for Prospects and Plays - White, AAPG, 1993
Play Fairway Analysis using GIS based Common Risk Segment Mapping - Cooper, CSPG, 2006
Use of Geographic Information Systems in Hydrocarbon Resource Assessment and Opportunity Analysis - Hood, AAPG Computer Applications in Geology, No. 4, 2000

As part of this, play fairway and opportunity risking analysis (including Common Risk Segment mapping) have been identified as key. These methods reduce overall exploration risk because oil and gas companies are (generally) less likely to bid for high risk acreage, or to work-up or drill prospects in high risk plays.

Barriers to Analysis

Traditionally block and opportunity ranking workflows have been incredibly complex, due to a number of factors, e.g.:

Data required to do the analysis often resided in multiple systems and required great effort to be integrated together.
Heterogeneous data formats between such systems meant data translation, which was time consuming and also introduced errors.
Due to the complexities data simplification was often required just to perform the analysis, which kind of defeated the object of the exercise.
The analysis could often only answer a narrow set of questions, and was not designed to be scalable or interated.

Often these barriers proved insurmountable, and many oil and gas companies have had to live without performing these types of analysis, especially within the pressure-cooker environment of a licensing or bidding round.

In addition, and as exemplified by the story of the exploration manager who told his staff words to the effect of "I'll tell you where we explore for oil, not some bit of software!", there are human elements at play here, e.g.

As Clare Bond and others have shown, geoscientists introduce their own interpretation bias based on their past experiences.
You can't know what you don't know, regardless of how clever you think you are.

Data Driven Analysis

Team-GIS Acreage Analyst is a tool that allows a geoscientist to summarise oil opportunities (e.g. leases, blocks, fields, etc.) based on the spatial relationship between each opportunity (e.g. oil lease, field, etc.) and multiple input data sets. The key to the tool is that it uses quantitative analysis, although the geoscientist can apply their own scoring schema and weightings to the analysis results.

Team-GIS Segment Analyst is a tool that helps users rapidly build common risk segment maps for play fairway mapping. Outputs from Segment Analyst can be used as an input to the acreage ranking in order to summarise play potential across the area, allowing explorationists to rank each lease based on a measure of prospectivity, as well as by any other factors (e.g. distance to infrastructure, environment sensivities, etc.). Again, the use of a computer-driven quantitative approach allows the user to rapidly repeat the analysis, and to analyse sensitivities.

It goes without saying that for either tool the input datasets must be valid and accurate, in the same way that a geophysicist's seismic interpretation is dependent on having the best quality seismic data available. And clearly the quality of the analysis is wholly dependent on the interpretation of the geoscientist operating the tools.

Significant Productivity Gains and Improved Decisions

Both tools described above allow oil and gas companies to start doing what the Cooper, Miller, White and Hood papers describe, without having to develop bespoke in-house tools.

The bottom line is that the tools provide oil and gas companies with significant productivity gains when performing important exploration analysis (i.e. creating common risk segment maps and performing acreage/opportunity screening). The benefits of this are:

The streamlined computer driven process is much easier than traditional manual methods, meaning that the analysis actually gets done in the first place.
The speed of the analysis means it can be run multiple times within a given work-cycle, allowing you to evaluate sensitivities and try out different theories and scenarios. This can dramatically improve your decision quality.

This in turn has the effect of significantly improving the geotechnical interpretation and this leads to a greater understanding of what's actually going on. This reduction in technical uncertainty significantly reduces your G&G risk.

Posted by Chris Jepps, Technical Director, Exprodat.

Shale Gas Well Reserve Mapping with GIS

Thu, 11 Mar 2010

Unconventional Gas

The interest in unconventional gas exploration has significantly grown over the last decade following advances in drilling technology and exploration methods for monitoring and extracting unconventional gas^[^1,2,3^]. Shale Gas has now become a target for new exploration and exploitation. Recent articles indicate the increase in the amount of Shale Gas plays that are being considered not only within North America but in Europe and across the world ^[^4,5,6^].

Basin and play resource and reserves are frequently monitored - both for regulatory requirements and for internal company estimates. As a result, companies need to regularly assess gas in place (GIP) volumes.

Shale Gas Reserve Mapping Example

Lets assume that a new drilling campaign is being developed and planned in a new onshore frontier basin. In this basin shale gas play mapping carried out by geoscientists using ArcGIS has identified possible locations to test the potential resource with an aim to create producing wells to extract the gas.

In this scenario a campaign of exploration / appraisal wells, producing wells and horizontal wells are planned and drilled over time. In order to monitor the wells over time, a reserve halo is defined around each well type at a given time snapshot during the drilling campaign corresponding to the reserve class, e.g. proved, probable, possible, etc. ^[^7,8^] . The reserve halos defined around each well, in this scenario, are fixed to an underlying grid for audit purposes. The reserve “value” for each grid cell will vary over time depending on the type of well.

The shale gas play resource is tested using exploration and appraisal wells. As the play is developed producing wells are indentified and come online with horizontal wells targeting the “proved” areas. The reserves based on these wells are calculated at various times throughout the drilling cycle.

Traditionally reserves are calculated using maps 'mocked-up' in spreadsheets based on measurements made by manually placing a grid over a well hardcopy map and the number of reserve class halo grid cells counted around each well location. This can be a time consuming process taking anything from hours to days depending on the number of wells attempted.

Mapping Well Reserves in ArcGIS

This example shows how ArcGIS can automate and improve the monitoring and management of well reserve mapping within a shale gas basin play, dramatically speeding up the old spreadsheet-based process.

ArcGIS uses location based geoprocessing to map the reserves based on a well location (point) layer and a fishnet grid that covers the shale gas play area. Rules can be defined in ArcGIS that map the reserve halos around each well (e.g. proved, probable and possible) based on the various reserve class methods commonly used within the industry ^[^7,8^].

In this example we're using a simple reserve halo arrangement. Where a well location triggers a “seed” grid cell for a new reserve halo the selection process creates overlapping halos, thus avoiding double counting of reserves. The resulting map not only shows the spatial extent of the reserves but allows an automated summary of reserves for a given area (see Fig. 1).

Figure 1. Reserve map showing producing wells and horizontal wells within the study area following an initial exploration / appraisal stage.

Using ArcGIS Desktop the reserve mapping process is automated - reducing the process time to just a few minutes (from hours / days). The reserve mapping results can be archived and used to assess (audit) the shale gas reserves over time. Mapping the reserves in GIS allows the user to summarise the reserves by any input polygon, e.g. play, basin, license or government boundary, etc.

Reserve Temporal Visualisation

ArcGIS can be used to animate the change in well class reserve over time. In this example, a number of simple reserve halos have been defined around exploration / appraisal wells, producing wells and horizontal wells showing the difference in reserve allocation over time.

The audit trail created in the GIS data attributes allows the comparison of the grid cell values over time. Within the GIS you can now quickly and efficiently compare reserves over time and see how the reserve map changes from being dominated by exploration wells, through producing wells to horizontal wells.

If the animation does not appear below you can view it at the Exprodat GIS YouTube channel.

Creating a Group Layer Animation in ArcMap

Animation within ArcMap can be used to visualise the change in reserves over time. The following information builds on our Petroleum GIS Tip 6: Animation in ArcGIS Desktop blog, although here we're building a group animation to animate the change in shale gas reserves over time.

In ArcMap create a group layer that contains several reserve map layers listed in time hierarchy.
Next, click View > Toolbars and then select Animation
In the Animation toolbar click Animation > Create Group Animation
The Create Group Animation allows the user to define which group within the table of contents you will use for the animation. The user can define animation transition. Click OK in the dialog.
To play the animation simple press play on the animation toolbar or you can export to an export animation file.

Summary

In the scenario above, automatic shale gas reserve mapping within ArcGIS has been proven to be a fast and efficient method for mapping and monitoring shale gas reserves over time, allowing:

Rapid mapping of shale gas reserves, e.g. based on an underlying grid.
Creation of an audit trail of well class reserve through time, which can be used not only for mandatory audit, but also for company portfolio reserve estimates.
Summing reserves by basin, government or local license boundary.
Calculation of overall GIP per reserve class per basin / license boundary.

Contact Exprodat to find out more about using ArcGIS to assist in shale gas reserve mapping.

Posted by Alex Davis, Senior GIS Consultant, Exprodat.

References:

1. Brian J. Cardott, "Shales Closing ‘Conventional’ Gap", AAPG Explorer, November 2008, p.78.
2. Clifford Krauss, “New Way to Tap Gas May Expand Global Supplies”, The New York Times, October 2009.
3. John Madslien, “All change as gas reserves soar”, BBC online article, 8th November, 2009.
4. R. Marc Bustin, “Gas Shale Tapped for Big Pay”, AAPG Explorer, February 2005.
5. Susan R. Eaton, “Utica emerges in Québec, Shale Play Extends to Canada”, AAPG Explorer, January 2010.
6. Louise S. Durham, “Poland Silurian Shale Ready for Action”, AAPG Explorer, February 2010.
7. Society of Petroleum Engineers, “Petroleum Resources Management System”, 2007.
8. Natural Gas website, “The Natural Gas Resource Base”.

Tip 7: Convert Date Formats

Mon, 15 Feb 2010

We sometimes find datasets containing attribute data that we can’t use effectively because the data is not stored correctly. A common example is a field containing dates where the field type is defined as 'Text'. This results in difficulties filtering the dataset by date, e.g. "display data older than X”. In this petroleum GIS tip we take the common example of an oil well dataset that has dates stored as text, and we'll fix it by using ArcMap’s field calculator.

To begin with make sure you have write access to the dataset. If the dataset is read-only you can create a new copy by exporting right mouse clicking on the dataset name in the table of contents and selecting Data > Export Data.

Firstly a new Date field needs to be created for the spud date data in the dataset's attribute table. To do this have the dataset as a layer in your ArcMap session and open the dataset's attribute table by right mouse clicking on the datasets name in the table of contents and select Open Attribute Table. Click on the Options button at the bottom of the attribute table window and select Add Field.

In the dialog specify a name for the field which must be unique in the dataset (and if the dataset is a shapefile the field name length must be ten characters or les ). From the Type drop down list select Date.

To find the newly created field in the attribute table simply scroll to the end of the table (to the right). To reformat the original text date data we need to use the Field Calculator, which allows you to perform arithmetic calculations and string manipulation functions on attribute data. This tool uses functions that you may be familiar with from Microsoft Excel. It also has an option to enter VBA code for more powerful functions (with Python to be included in ArcGIS 10), but don’t worry - we’re not going to do any VBA here!

In the attribute table right mouse click on the field name (header) of the new field column that was created above and select Field Calculator in the context menu.

Select Type: String and double mouse click on the function Format( ) which will insert the reformatting function into the text box. Click inside the parantheses and then select the field from the fields list which contains the text formatted date values, which in my case is “SPUDDATE”. Double click this to move it into the expression.

Next, click in the text box so that your cursor is between the field name and the closing bracket. Type a comma and a double quote. Next specify the format of the text string in date code (e.g. 1975-03-31 will be YYYY-MM-DD, 02/12/1982 will be DD/MM/YYYY or MM/DD/YYYY if US style). Finish with a double quote so that it is similar to the text below (your field name and date style may well be different to mine):

Format ([SPUDDATE], "YYYY-MM-DD")

Click OK and the Field Calculator will iterate through the records in the table translating the date data from text into the format you have specified.

You can now filter the dataset for features with particular dates. In my example I’m only interested in wells that were spudded after 1980, so I can build a Definition Query to do this, as shown below.

The dataset will now be filtered in both the map and the attribute table. Note I can easily remove this filter by deleting the query text in the Definition Query tab in the Layer Properties dialog.

Data management tasks often need to be repeated and in ArcGIS they can be automated so that users don’t have to do repetitive manual work. Exprodat has experience in creating customs scripts to alleviate the manual work for users so that the users can spend more time using the data instead of managing it. If you’re interested in our spatial data management services please contact us using the 'More Information' link at the bottom of this page.

Posted by Rob Clark, GIS Consultant, Exprodat.

Around 2800 Blocks in 80 days

Fri, 05 Feb 2010

The UK government launched its 26th offshore oil and gas licensing round last week. The round includes new "Frontier" licences, with an extended nine-year exploration term, as well as some new tax breaks to stimulate activity. Blocks are being offered in all UK territorial waters for the first time in 12 years.

It’s a massive round, with 2818 blocks on offer. But here's the catch - submissions for the round must be received by April 28th 2010. So, as head of Exploration in your organisation you go home at night and try to draw up a plan... A quick peak at your calendar tells you there are 64 working days between the round announcement and the deadline. Add in a few weekends and some overtime and that’s around 2800 blocks in 80 days, as Jules Verne might say.

You have already been working on suspected 26th round blocks for the last x months in your core area, and you're already well on your way to preparing this application. So you can ignore those 300 blocks for now. You've also made a strategic call not to explore in a couple of high risk areas, which enables you to ignore another 1000 blocks. Great – this is easy!

For the frontier blocks you want at least 15 days to prepare and write your application (although that's a bit light, but hey, the outsourced drafting guys are really good and you can touch type at 60wpm). You also want a further 20 days to generate some prospects in these non-core areas before you write-up.

According to your plan you now have 45 days to look at 1518 blocks. By any calculation that’s a tough ask. You have a team of 3 exploration geoscientists champing at the bit, but the next morning you call a meeting. "Guys -", you tell them, "there's no way we can do it - you'd each need to make a call on 12 blocks every day for the next 7 weeks, as well as complete the work on our core areas!" You decide not to mention that this would also mean working weekends, and so you to stick to the stuff you already have a good understanding of.

Little wonder then that Hannon Westwood are already speculating that "existing majors and super-majors will make only very selective applications in existing core areas", despite estimated un-risked potential reserves of 35 billion boe across the 26th round acreage.

If only there was a way to rank all the blocks based on all your available data that wasn’t really time consuming. Say, in a just few days.

At Exprodat we see GIS as a key component for supporting and improving the Exploration process, including risk assessment, opportunity screening and ranking. As such we've commercialised some tools that enable users to do just that. One such tool is Team-GIS Acreage Analyst (TGAA), which allows users to rapidly rank petroleum opportunities (such as license blocks) based on multi-source, multi-disciplinary data.

On announcement of the round I decided to run a little test. I took the list of blocks on offer from the DECC website and put them into ArcGIS Desktop (that’s the map you can see above, with the 26th round blocks shaded light yellow). I then added some input data layers, such as facilities, pipelines, play YTFs, reservoir presence, etc. and ran an analysis using TGAA.

OK, I had to make up some of the input data, like play YTFs (so don’t look too closely at the results), but it took less than 5 minutes to run. 5 minutes! All I then had to do was score the results based on my assumptions of prospectivity. That process took a whole 15 seconds.

So, with the early screening work now done (see map below), I still have 63 days to look at the best areas in detail and start generating prospects, with the added bonus that no-one is working weekends.

Hopefully you can see that an Exploration department using this kind of technology can gain a considerable ‘jump’ on the opposition, not just by focussing on superior areas much earlier in the round application process, but also by making accurate data-driven decisions rather than relying on “geo-hunch” or conforming to historic corporate behaviour.

Posted by Chris Jepps, Technical Director, Exprodat.

Tip 6: Animation in ArcGIS Desktop

Thu, 28 Jan 2010

With 48 toolbars at version 9.3.1 ArcGIS Desktop has a lot of hidden functionality that many users may be unaware of. One such toolbar is the Animation toolbar which allows you to animate data to help identify spatial trends through time. The functionality is not obvious at first but ArcGIS Desktop’s integrated Help covers animation in quite some detail. In this blog post we’ll cover the basics of animation using the example of animating the North Sea wells spudded by a particular oil company over time, a timely example with the UK’s 26th licensing round around the corner.

To begin, open the Animation toolbar by clicking on View > Toolbars and then select Animation.

Let’s look at the terminology used within the Animation toolset:
• Keyframe: a keyframe is a snapshot of the visual state of the map at a point in time.
• Group Animation: an animation that loops though layers within a group layer.
• Time Layer Animation: an animation that uses values within an attribute field.
• Track: an ordered collection of keyframes transitioning through time.

In this example we’re going to create a Time Layer Animation to animate the wells that were spudded over time. Select Time Layer Animation from the animation menu and then populate the dialog as below.

1. Select the layer to be animated.
2. Select the field that contains date data.
If the date is stored in a string format then in the format list you can select the correct format of the string e.g. DDMMYY.
3. Select the time interval to loop through data.
4. If you wish to retain the data on the map then check Animate fields cumulatively. If this is not checked then when animating from one period of time to another the previous period’s data will be removed from the map.
5. Specify a text string to be used as a label in front of the Start Time field value when posted on the map.
6. Click Create.

If data was to be hidden sequentially during time of the animation you can specify a field name for End Time (uncheck ‘Animate fields cumulatively’).

Click on the ‘Open Animation Controls’ tool (right-most tool) on the Animation toolbar to open the animation controls, then click on the play button to play the animation.

You can modify the properties of the time track at any time by opening the Animation Manager on the animation toolbar. Select the Tracks tab, select the track and then click on Properties.

You can then export the animation to a video (*.avi) file, using the following workflow:

1. Click on the Animation menu in the animation toolbar and select Export to Video.
2. Navigate to a target output folder and specify a name for your video.
3. The next dialog will provide options for video compression. If you have a higher quality codec (compressor program) then you can select it in this dialog. Higher quality codecs can also be obtained from the Internet e.g. DivX.
4. Click OK.

If the animation does not appear below you can view it at the Exprodat GIS YouTube channel.

The above example is a simple ArcGIS Desktop animation showing BP wells drilled in the UK North Sea. This uses a graph which also tracks its data over time. To do this first create the graph using the default wizard in the Tools > Graphs menu option. Then, when proceeding through the animation workflow above, use the graph’s data source (e.g. a table or a layer) when selecting the layer to be animated in the ‘Create Time Layer Animation’ dialog. ArcGIS will automatically synchronise the timings of the animation between layers. With the graph window open, position the window over the map at a suitable location for it to be recorded. Note that anything positioned over the map at the time of the export will be recorded.

Posted by Rob Clark, Consultant, Exprodat Consulting Ltd.

How Big is your Training Gain? Part 2

Thu, 21 Jan 2010

In part 1 of “How big is your training gain?”, I provided a simple spreadsheet model that readers could have a play with and visualise their potential training gain. Well how did it go? In part 2 I'd like to present and discuss a couple scenarios that I came up with.

Scenario Modelling

Scenario 1 - First training at 6 months: a new geoscientist with no ArcGIS skills, she manages to pick up some knowledge from colleagues during her first 6 months on the job and learns a few things on ArcGIS, along with geological knowledge of the plays the company is looking at. Six months into the job she gets sent on an introductory ArcGIS course and acquires a much greater skill level almost immediately. The ‘training gain’ for this new geologist over where she would have been in three years without training is represented by the area between the two lines (Fig 1), i.e. the ‘training gain’.

Scenario 2 - First training on day 1: look at the difference in skills acquired by the geoscientist (and by implication her organisation) if she had the same training on day 1 of her employment (Fig 2). The training cost was the same, but the company’s return on investment is bigger. We can argue about how much skill she gained from the training and this will vary from person to person, but she will gain skill and the earlier she is trained the bigger the return on investment. Play with the model to get a better idea of what this looks like.

Scenario 3 - Second training at 12 months: the same geoscientist gets a second course at the end of Year 1 so that she can continue to hone her skills (Fig 3). Her skill level is significant, and she begins to assist other users in the company, so her skills begin to positively affect the productivity of others in addition to her own gains. Notice that we have imposed a law of ‘diminishing returns’ on training, in that we’ve allowed the modelled ‘skill gain’ to be in proportion to what is left to know.

Scenario 4 - Third training at 15 months: the same geoscientist then decides to specialise in supporting play fairway mapping using ArcGIS tools across the groups within her company and asks for a 3rd course 3 months later (Fig 4). She is now, probably, approaching expert user level skills and is actively mentoring and coaching other team members. Additionally, her productivity is significantly higher now and play fairway maps are available to her team more rapidly so that more scenarios can be explored before licence bids need to be tendered.

Take Away Messages

1. It’s only a model – use with caution!
2. We don’t assume that all learning happens in formal training environments – and neither should you, but the question is how much can we realistically expect most busy geoscientists to pick up, while they are getting on with projects?
3. If your organisation wants to maximise it’s effective in-house skills base it needs to train, and training early and training frequently will always have greater impact.
4. If you leave training until ‘later’ you might pay the same or even a little more for the training, and you will certainly have a lower return on investment!

This is a simple model meant to allow those of us interested in professional development to think a little more deeply about the impact of training. We have intentionally kept it simple using only four parameters and readers are encouraged to have a play with it and see what conclusions they draw from modelling their potential ‘training gain’!

Part 3 will explore the assumptions and limitations of this model and what we might do to improve on it. I will also pull together any feedback that comes in!

Posted by Chris Skelly, Training Manager, Exprodat Consulting Ltd.

Landfill Sites vs. Acreage: Worlds Apart?

Wed, 20 Jan 2010

As if running a GIS business wasn't hard enough work, I recently signed up for a part-time MSc course, run by UNIGIS. One of the fun things about a course like this is that it takes you in to areas you wouldn’t normally venture.

One of the exercises I’ve just completed was to identify potential landfill sites on the Wirral Peninsula, in North West England. We were given a variety of data sets and references to some legislation documentation on the subject, and told to produce a map to support our findings. The extremely simplistic and selective workflow I developed is shown below.

A number of things struck me about this exercise, and I thought it would be interesting to compare the process with something similar in the E&P business, selecting acreage opportunities. Both processes potentially use an enormous amount of data to come to an ‘answer’, albeit of totally different types. Both have a significant financial risk associated with making a wrong decision. The outputs even look the same; the sort of red/green traffic light map displays we’re used to seeing in the E&P industry.

However, the differences between them are more interesting to me. I was struck (and vaguely reassured) by the amount of process and legislation that need to be adhered to when locating landfill sites. And if anything, there is even more data to take in to account in making a landfill site decision than there is in the E&P business. The environmental sensitivities through the impact on people’s lives, wildlife, possible ground contamination, etc. require the process be subjected to the most detailed of scrutiny. Detailed network analysis is required to position them just the right distance from infrastructure. Processes need to be consistent, repeatable and documented to pass the many gates to planning acceptance.

Compare this with Exploration decision making. In my experience, most decisions are made based largely on personal and historical biases, selective data analysis and variable levels of peer review. The processes used to get from raw Exploration data to selection of acreage are often highly variable, between individuals as well as assets. Even the same person is unlikely to do it the same way twice! Audit trails often exist only as PowerPoint presentations, if at all. Sure, there are legal, environmental and commercial constraints that are adhered to, but this rigour often doesn’t extend to the Exploration team. The outcome in the Exploration case may be slightly different in that it doesn’t always impact people’s lives in the same way a landfill site might. But it does commit the company to potentially enormous financial commitments.

At Exprodat, we provide GIS software and training specifically targeted at Exploration, to help companies to standardise processes in the same way as is required in other industries. So often when we talk to clients their response is that ‘we don’t have set ways of doing this’. It seems to be accepted in the E&P business that it’s pretty much OK for geoscientists to work under very loose constraints to facilitate fresh insights. The concept of ‘interpretation’ in Exploration holds sway, compared to, say, ‘data analysis’ in the landfill example. This seems to provide the geoscientist with several degrees of freedom in their approach that those working in the public sector don’t seem to have. I wonder what the impact would be if Exploration geoscientists were asked to select landfill sites instead!

Gareth’s landfill site model for the Wirral: treat with extreme caution!

Posted by Gareth Smth, Managing Director, Exprodat.

Tip 5: GDB Attribute Domains

Mon, 11 Jan 2010

Attribute Domains for ArcGIS File Geodatabases are arguably the most important data quality tool for geoscientists creating play fairway mapping data. As such we thought it would be useful if the next in our series of GIS Tips explained how to use them.

There are probably three types of ArcGIS users reading this:

Users that already understand the power of Attribute Domains.
Users who are new to ArcGIS and have never heard about Attribute Domains.
Users who are ‘old hands’ now, but developed most of their skills when Shapefiles were all the rage (i.e. they haven’t heard about Attribute Domains either!).

Those of you in user groups 2 and 3 read on, because this tip should be really useful.

Improving Attribute Data Quality

Have you ever been in the situation where you have two well databases and you are trying to Join or Relate them (see Tip 4)? This should be a few seconds job but almost never is because the well names never match up perfectly.

Often a string (or 'text') attribute value, like the name of a well, only has to be out by a single character to cause a Join or Relate operation to fail. If you are careful, you’ll notice that you have some sort of string mismatch and you’ll spend hours trying to rectify the situation. That’s if you are being careful and noticed the error to begin with. This is a good example of a common problem with attribute fields, not because many geoscientists will be creating well databases themselves, but because we’ve all been frustrated by them – we understand the pain!

However, if you create your own play fairway maps, there is an even more frustrating and potentially much more expensive problem if you mistype an attribute value. In fact, it could cost your organisation a license that you might otherwise have taken on, just because someone entered “Oil Mture” or “oil mature” or “oil_mature” instead of “Oil Mature”.

Tackling the Play Fairway Attribute QC Problem

Play fairway analysis and mapping often requires one or more of the common risk segment layers to be digitised from existing data and a spatial data set to be constructed. Perhaps you have a map like the one below?

Lets assume that the black rectangle shows your area of interest and that the challenge is to create a file geodatabase by capturing the polygons within this region. The usual workflow will be to capture the polygons by screen digitising this area. Users will all have their preferred method of structuring this capture efficiently, as has been done in the image below.

So far this is what all geoscientists would do in order to capture the information from this map. And then for each polygon captured, 'A' through 'H2', users would enter into a field called soemthing like Source_Maturity (or some unreadable abbreviation the aforementioned), each polygon’s type:

The greater the number of polygons that need to be attributed, the greater the potential for typos. In the example above you’d probably catch the error(s), but when there are dozens of polygons and/or more than one person entering data, the amount of QC work increases dramatically.

Using Attribute Domains

To avoid these potential typos, and to simplify and standardise the data that you allow to be entered into your attribute database simply use Attribute Domains. A “Domain” denotes all valid entries into a particular attribute field. One really cool feature is that when you establish an Attribute Domain for a particular File Geodatabase, it can be applied to any Feature Class that is stored within it, meaning it can be used multiple times. This can be a significant productivity gain.

So, instead of laboriously typing the source maturity type into the attribute table for each polygon created, when the Attribute Domain has been created you are presented with a drop down menu. Point – click – no errors possible – accuracy and huge time savings to boot.

So how to apply an Attribute Domain to your feature class? In ArcCatalog, simply right-click on the feature class file you have created in order to digitise the polygons into and open the context menu > Properties. You are presented with the New Feature Class window (below).

Select the Type field (if it doesn’t exist, create it) and set the domain to whatever you have created for the File Geodatabase. In our case, we called it “Charge”. That is all you have to do to use an existing Attribute Domain within a File Geodatabase, but how do you create the attribute domain in the first place?

Creating Attribute Domains

Again, in ArcCatalog, right-click on the File Geodatabase in which your feature class is stored and from the context menu select Properties to pull up the Database Properties dialog, as shown below.

Select the Domain tab and fill out the dialog, as above. The “code” is an abbreviation in this case, but it could also be an integer value. This isn’t a complex example – you on the other hand might have subtle and important definitions that you can make other users aware of without imposing any additional overhead on their attribute data entry by developing or using existing attribute coding systems.

Two Types of Domain

There are two types of domain that users can specify: range and code based domains. These provide even more database QA. Suppose you know that in a particular basin, the source layer has never been found to be more than 1000 feet thick: set Field Type to Integer and Domain Type to Range and specify a range of 0-1000. This way no negative values (an impossibility) or values over 1000 feet (suspect until proven!) will get entered by mistake.

In the Database Properties window, above, we’ve shown a case where a Text Field has been coded with four values. Aside from the already mentioned improvement in data entry speed – there are now only four possible values (unless you edit this domain) – users cannot by accident or intent create other values on-the-fly. This will greatly improve the accuracy of database queries.

Advantages to Using Attribute Domains

Firstly, you will improve your quality control and the more people using the domain to standardise attribute field the greater the value of this QC. Secondly, with only a small setup overhead you have reduced the data entry time and this can be a huge savings both for yourself and any team members working from the same file geodatabase. Thirdly, you can bet that you are one of the few people in your team who now know the value of using Attribute Domains in ArcGIS. Which makes you look good.

Posted by Chris Skelly, Training Manager, Exprodat Consulting Ltd.

How Big is your Training Gain? Part 1

Thu, 07 Jan 2010

Exprodat is focused on helping our clients in the petroleum sector make better use of Geographic Information System (GIS) technology and we are sector leading specialists in the use and application of ArcGIS technology from ESRI to E&P spatial challenges.

Every organisation that takes on board GIS wrestles with the question: how much training do we need to purchase? Because one of our three core services is GIS training, we get asked this question a lot, maybe not directly, but when organisations talk to us about what we have to offer, this is always an underlying question. So, is there a training metric you can calculate that will tell you how much training each of your geoscientists needs in order to be an effective user of GIS tools?

Training Gain

I'd like to propose using a metric that we’ll call the ‘Training Gain’ as a potential solution to help decide how much training you need, while not actually suggesting that this single metric will be the answer to all questions. I think of Training Gain as representing the total increase in productivity and effectiveness that training might make to an individual’s performance, versus their likely performance if they were just left to learn-"on-the-job".

We have created a very simple model (available for you to download as an Excel file) to help visualise the impact that training can have on a member of staff.

For technical boffins, the equation is:

SLTM = SLLM + ((1-SLLM)*(MSG + (NTE * SGE)))

where,
SLTM = skill level this month
SLLM = skill level last month
MSG = monthly skill gain from on-the-job-osmosis
NTE = number of training events last month
SGE = skill gain from one training event

I suggest graphing the level of technical GIS skill of an individual geoscientist over 36 months (it could be any length of time, but let’s keep it simple) as three years is a useful period of time in terms of learning new skills.

Model Assumptions

The following assumptions apply to the model:

The level of technical skill in GIS is essentially finite, so the model imposes a scale on GIS expertise of 0-1, where 0 skill represents a geoscience professional with no GIS expertise and 1 represents a professional with an expert skill level.
Rarely would we expect a geoscientist using ArcGIS to become a GIS expert, but with training and experience, in 3-5 years, it may be possible for geoscience professionals to get close to expert level if they were so inclined and had an aptitude for GIS.
Professional geoscientists acquire ArcGIS software skills on-the-job through self study and through transfer from colleagues, even without training. This is a continuous process, but in busy organisations this might also be a very slow process.
Training events provide a second method of learning and we believe that the impact of training events is in being concentrated and focussed skills acquisition from GIS experts.

Model Parameters

Experience at Start: how much ArcGIS skill did the staff member have at the beginning of the modelling period, on a scale of 0-1. If they have never even heard of ArcGIS then assign a 0. If they are familiar with the concepts and ideas, but have done nothing more than look over someone’s shoulder, give them a starting point of 0.05 (perhaps still too generous!). Maybe they’ve used ArcGIS to open up someone else’s map and have a look? Then score 0.1. Like all modelling use your best judgement, this is subjective, and the goal is to gain a better feel for what training gives you over a period of time.

Skill gained through on-the-job experience: a steady monthly gain in skill from using and working with others who are using ArcGIS. If we posited that you could acquire an expert skill level in three years (36 months) of simply using ArcGIS on a frequent basis that would mean an annual skill gain of > 80% on top of what you already know. For all but a few exceptional individuals that is far too high, perhaps 12% each year or 1% each month is a more realistic value. Again, play with the numbers, what seems right to you?

Skill gained through Training Event(s): the skills gained from a single training event is a discrete nugget of professional development aimed solely at raising an individual’s skill level. This parameter represents the amount of skill gained as a proportion of what you already know. It is subject to the law of diminishing returns! In other words, you can only ever learn a proportion of what is known as you increase in skill level, additionally learning (although perhaps critical) will only bring smaller increments of knowledge.

Training Events: this parameter allows the user to set the timing and number of training events that an individual undertakes during the period being modelled.

To Be Continued...

Please feel free to download the model (Excel file) and have a play.

In Part II of “How big is your training gain?”, I’ll provide a couple scenarios that I’ve run, but if this is an area of interest to you – you should have a play with this very simple model first, without me biasing you any further!

Posted by Chris Skelly, Training Manager, Exprodat Consulting Ltd.

Santa's Ballistic Delivery System

Mon, 21 Dec 2009

“So,” said Santa, his grin widening, “we can get rid of the reindeer?”.

He pushed back from his desk and turned to gaze through the glass wall that separated his office from the staff canteen. The reindeer sat together at one of the long tables that were arranged in rows in the canteen. They weren’t happy. The R&D department had finished whatever it was that they’d been up to for all these months and were going to demonstrate it to Santa, today. For some weeks now, rumours had been circulating about what they were up to, rumours involving Elven jokes about what they would be doing with the stable block and the reindeer themselves.

Santa chuckled and turned back to the presentation. The chief Elf, a cheerful soul at the best of times and now sporting a smile so wide that it was a wonder that the top of his head didn’t just tear off, advanced to the next slide, which showed a bar chart with two bars, one big and one small.

“Yes, we can. The new system has 500% of the current delivery capacity and can be expanded as necessary, by bringing additional units online”. He used a toy snooker cue to point at the higher of the two bars and added “It’s future proof.”.

He gathered up his papers and then said, “Ok, let’s go and see the test system.”

“Curtains for us then. I wonder what it’ll be – reindeer roast or burgers?”. Donner said, chewing one of his hooves.

“You guys need to do some exercise, you’re getting fat. No need to let yourselves go, just yet…” said Santa to Donner as he walked past with the Chief Elf.

Santa’s Little Helper, who for the benefit of narrative confusion was a 6ft 5inch human being called Bob with a ponytail and predilection for Prog Rock, swivelled his chair round and began to speak. “So, what we here have is a state-of-the-art delivery system, based on ballistic missile technology that we picked up for a song. Pin-point targeting, laser-guided whatnots and some great whojiwhatsits. Any questions?”. Santa rolled his eyes and shook his head, giving him the idea for a great new toy.

“We load the presents into our delivery pods, one or more per city, town or village, and then fire them off. The firing sequence is configurable for a full 24 hour midnight-following delivery schedule. You press the button and then sit back and watch the show. “ filled in the Chief Elf.

“Sounds fab – can’t remember the last time that I had Christmas off. Sort of gets rid of my raison d’être, though, don’t you think?”

A momentary silence followed, which was broken when Bob burped quietly. “Show him the simulation” suggested the Chief Elf to Bob. “Ah yes. We’ve obviously got a test system outside – the thing firing presents into the sky, you may have noticed it?”

“Yes, bit difficult not to, it’s rather noisy. I like the wrapping paper on the pods, though.”

“Well, in order to give you a full understanding of what’s going on, we’ve created some simulations using a couple of cool free tools. The first one’s by ESRI, a leading producer of GIS software - ”

“A great company, who we’re partnering with on a number of projects”, piped up the Chief Elf.

“and the second one’s by Exprodat, a leading implementor of GIS solutions to the Oil & Gas E&P business. They gave us an early Beta, works a treat though.”.

“Really, I didn’t realise we were using something from them. They’re great too, we use their Acreage Analyst tool to define areas where children are being really naughty, enabling us to scale our delivery solutions accordingly. Saves us a pile of cash.”

Santa was looking a little bored. He’d taken his hat off and was playing with it. He sensed that the others were looking expectantly at him, pulled his hat back on and spoke. “Enough with the shameless product placement – you can put some links at the end of the presentation. On with the demo.”

And here, dear reader, you can see what they saw (download ArcGIS Explorer KMZ file - the screenshot below doesn't really do it justice).

“So we can fire the presents from here? No need to fly? Nice”, said Santa. “Looks great – not enough presents though!”.

“Yep, we’re limited by the RAM on this machine, could do with an upgrade. We’d be able to simulate more present trajectories. But, as we’ve said, the production system will be able to handle a full 24 hour delivery cycle to all the children on the planet.”.

Santa pondered for a moment.

“A thought occurs – what happens when the presents land? Surely they’ll be smashed to pieces?”

“No, we’ve thought of that”, said Bob with a self-satisfied grin. “A miniature parachute deploys just before impact, and the present floats gently down the target chimney. It took a while to adjust the guidance systems to cope with it. We’re thinking of licensing the technology back to the military.”

“But what if the fire’s lit, or if they don’t have a chimney?”

“Err. That’s a good point. Didn’t think about that. One for version 2?”

“Well…” started Santa, but was interrupted by the Chief Elf. “It’s got something you don’t have now, though – you can punish the naughty children.”

“Punish? I thought not getting presents was good enough? What sort of punishment?”

“Bombs”.

“Are you mad?”

The Chief Elf came to the sudden realisation that this had been a very bad idea. Bob, however, had not yet done so. “We’ve trialled it – we can drop a bomb straight down the chimney, taking out the naughty child entirely. Obviously ordnance has a cost, but over a ten year period the savings on database administration more than make up for it.”

“You’re both on present-wrapping duty for the next Millennium.” Santa wandered back to the canteen. The Elves were already dismantling the new delivery system and packing it back into the crates that it had come in. News had reached the reindeer and they were looking a lot more cheerful. Santa took a deep breath and walked over to them.

“Hey guys, how’s my A-Team? Got everything you need? New stable, perhaps? Extra green grass? Whatever you want, ask the new Chief Elf, you’ll get it.” Santa closed his office door, walked over to his desk and sat down heavily in his chair. Sometimes it was tough being Santa. He needed a holiday. He picked up a piece of paper and fiddled with it. Then he picked up a pen, wrote Merry Christmas on the piece of paper and smiled to himself.

Posted, with Christmas wishes, by Ross Smail, Head of R&D, Exprodat.

Adding Cross Sections to ArcGIS Explorer

Thu, 03 Dec 2009

ESRI’s ArcGIS Explorer (AGX) is a free downloadable GIS viewer which allows viewing of a wide variety of spatial data in either 2D or 3D (Globe) display modes. In Exprodat’s R&D team we’ve been working with the AGX Software Development Kit (SDK) for a few months and have been impressed with what you can do, out-of-the-box. Naturally some tasks require you to do a little more work, and the one we’ll describe here is one of those.

A colleague needed to place a geological section in an AGX presentation, in its correct orientation, and asked for our assistance. He could have done it in Google Sketch-up, but we thought that we could probably create a tool in AGX that would allow us to do it more easily, and repeat the operation whenever we wanted. He supplied us with an example section, as a KML file, that referenced a COLLADA model. We’d vaguely heard of COLLADA (it’s basically an XML schema that enables the definition of 3D models) and took a quick look at the reference manual, and the example section.

One of the many things that COLLADA allows you to do is to define a mesh onto which an image is applied. The simplest mesh onto which a rectangular planar image can be projected is rectangular – i.e. defined by 4 corner points. These corner points are defined by X,Y,Z coordinates, meaning that the mesh can be oriented as required. Looking at the example file, we found that the KML file that referenced the COLLADA .dae file contained a Lat-Long insertion point, some orientation information and some scaling information. This allows the COLLADA model to be inserted, rotated and scaled to meet requirements.

Having worked out how the example file worked, we designed a tool, based on an AGX dockable window, to allow the user to drag out a line representing the section. We added editable text boxes to the window, which we populated with the start/end XYZ values for the section, allowing the user to fine-tune the section placement. We wrote, with help from the internet, some code to calculate the bearing of the line. We then added file selection controls to allow the user to pick an input image and an output KML file (which needs to be placed in a subfolder, as the KML file references files placed in subfolders).

Having created the basic user interface and some of the more generic code, we then worked on the code to actually do the work. It occurred to us that it might be easier to scale the model directly in the COLLADA file, by scaling the mesh, and then handle the rest of the work in the KML (the insertion point and orientation). To do this, we extracted the input image width (by loading it as a System.Drawing.Image and then looking at the width property), then used the length of the line the user had defined to calculate the scaling. We then wrote a COLLADA writer class to generate the .dae file, which we stored in a subfolder called models, mimicking the structure of the models exported from Google Sketchup. The image was copied across to a subfolder called images and then the KML file was created in the main target folder.

We gave the tool a spin and noted that the far end of the section (away from the insertion point) ‘stuck up’ in the air. A quick think made us realise that this was due to the earth’s curvature – the earth’s surface drops away from the base of the section. In order to correct this in a ‘quick and dirty’ fashion, we worked out a routine to drop the far end of the section by the height that it was initially above the surface. Doubtless COLLADA would allow us to do this more elegantly, fitting the base of the mesh to the surface as we went, but that’s something for another day.

So, there you have it, a tool that allows you to place sections on AGX in their correct orientation. Note that you can amend the Z-value of the insertion point to drop the section below the surface, although this then leaves you with the problem of how to actually see it (see the attached documentation for some tips about how to do this).

We've made the tool available fo download here (1.2Mb zip file) so please feel free to give it a spin – the download includes the AGX 900 Add-In (.EAZ file) and a PDF containing instructions on how to install and use the Add-In.

COLLADA looks like it has some legs for GIS, allowing you to create a single complex model which you can then position and orientate to meet your requirements, allowing the construction of animations and the like (or a simpler version of the Enormous Arrows tool that we've previously blogged). We’ll be investigating these possibilities over the coming months and will be posting examples and samples as we create them.

Posted by Ross Smail, Head of R&D, Exprodat Consulting Ltd.

Tip 4: Joins and Relates

Fri, 06 Nov 2009

One of the main issues that users seem to need a bit of help with in ArcGIS Desktop is the whole Joins and Relates 'thing'. I guess this comes up a lot in the petroleum sector as users are often trying to Join pick data to wells, or Relate production data to fields, etc. As such we thought it would be useful if the next in our series of GIS Tips walks through the process of how to set-up Joins and Relates.

Joining Attribute Tables

A join is a means of appending a spatial layer and a data table (or two separate data tables) together based on a common attribute or field. Joins work best when the tables/layers have a 'one-to-one' relationship (i.e. a single row in the external table corresponds to a single spatial feature). This is useful when you would like to label or symbolise your spatial layer using data held in an external table, or export a layer with rich attribution. For an example see the schematic above (taken from the ArcGIS Desktop help):

To Join a layer to an external data table follow the simple steps below:

Open the context menu of the layer that is receiving the join, by right clicking on the layer name.
Select Joins and Relates then select Join...
Choose the field in the original table to base the join on.
Choose the table to join to this layer.
Choose the field in the second table to base the join on.
Choose to keep all records or only the ones that match the joined table.
Click OK.
Open the context menu of the layer, and select Open Attribute Table. All field names will now be prefixed with name of the original table to show the join.

Relating Attribute Tables

Table relates associate data tables without permanently appending them to the base dataset like in a Join. Relates are designed when the relationship between the spatial layer and the external table is 'one-to-many' (i.e. there are many rows in the external table corresponding to each spatial feature). To Relate a layer to an external data table follow the simple steps below:

Open the context menu of the layer that is receiving the relate, by right clicking on the layer name.
Select Joins and Relates then select Relate...
Choose the field to base the relate on.
Choose the table to relate to this layer.
Choose the field to relate this table to the original layer.
Type a name for the relate.
Click OK.
Open the attribute table, click on Options, select Related Tables, and select the relate.

Posted by Chris Jepps, Technical Director, Exprodat Consulting Ltd.

Tip 3: Getting Help

Tue, 03 Nov 2009

One of the problems I faced when starting to use GIS was that there was so much to learn, and so many ways to get stuck! Often finding the best places to get answers to problems was something of a rights of passage. Things have changed since then of course - a lot of oil companies these days employ dedicated GIS support staff that users can ask, but what do you do when the support guys put their map reading skills to use while trekking around Nepal? Fear not, the next in our series of GIS Tips takes a bit of a detour and looks at sources of information available for getting help with ArcGIS.

First off, there's the official ArcGIS Desktop online help produced by ESRI. This contains lots of information on how to use the generic tools, and is an excellent place to start when you want to learn the basics of the application.

Secondly, there is lots of useful stuff posted on the ESRI 'Resource Centers' website, including links to free data sources, templates and tutorial movies (these are actually available in the online help but until I found them on the 'Resource Centers' pages I hadn't noticed them). The 'Resource Centers' also contains a link to the ESRI 'Support Center', and I've found this to be extremely useful over the years. If you've ever had a problem with ArcGIS the chances are that someone else saw it before you, has figured out how to resolve it, and posted something in the support site 'Knowledge Base'. Failing that, try contacting ESRI Support - you're probably paying for 'support and maintenance' so you might as well get your money's worth!

For a slightly 'behind-the-scenes' view on what ESRI is up to there are plenty of ESRI blogs to check out. These can be interesting when you want to see what's coming in future releases or if you want to get additional information on a specific product, such as ArcGIS Explorer. There are so many blogs that its often easier to subscribe to them via an RSS reader (such as Google Reader, shown below), so you can check them all from one interface.

Last but not least there are a whole host of other independent ArcGIS resources out there (like Exprodat's own blog site, also available via RSS). I'd like to pick out one in particular which is called 'GIS Tips & Tricks' - its a relatively new site, nicely laid out and puts up useful generic ArcGIS based workflows, including workflows using common third party products. It also has an RSS feed so that you can add it to your chosen RSS reader application.

So, there's no excuses for getting stuck next time your GIS support has gone on holiday...

Posted by Chris Jepps, Technical Director, Exprodat Consulting Ltd.

Enormous 3D Arrows in the Sky!

Mon, 26 Oct 2009

ESRI’s ArcGIS Explorer (AGX) is a free GIS viewer which allows the user to view a wide variety of spatial data in both 2D and 3D (globe) display modes. We’ve been working with the latest version, 900, since it was in Beta, and have been impressed with its new Software Development Kit (SDK) and presentation capabilities.

To help us get to grips with AGX 900 we decided to try and create a presentation recounting the exploration history of the North Sea. This took us down some unexpected avenues, and enabled us to take a closer look at the SDK. This blog discusses one of our diversions, which led us to develop a tool that enables the user to create (in big wavy letters) enormous 3D arrows in the sky (you can download our tool at the end of this posting).

We wanted to highlight an area in our AGX presentation, but we didn’t want to use a note icon as they are of a constant size. No, we wanted to do something more complicated and time-consuming. We thought about it for a while and determined that YES, 3D arrows were definitely what was required. So, where to start?

Creating a simple flat vertical arrow

It occurred to us that creating a KML file containing the arrow would be the best thing to do, as we knew a little about creating KML. We’ve subsequently observed that we could probably do it using AGX graphics, but if we wanted to transfer these to another package, we’d have to write them to an interchange format anyway, so KML turned out to be a good choice.

We first worked out how to create a single flat arrow pointing vertically downwards at a target location on the planet. We did this using a template-created ArcGIS Explorer button, using VB.NET code, which we wrote in Visual Studio 2008 Professional (you could use a free Visual Studio Express edition if you don’t have the Professional or higher version). The prototype code worked, but what we really wanted was a 3D arrow which we could easily define the location of, and generate in a repeatable fashion.

Creating an Arrow Creation tool dialog

AGX’s SDK gives us the ability to create dockable Windows, which can contain a variety of controls. We created one, using the DockWindow template that comes with the SDK. We added some controls, including a button that executes the TrackVector method of the MapDisplay class, which allows the user to define a vector (a polyline with two points) by clicking at two points on the map, the second of which is the target location for the arrow. This vector is then processed to retrieve the bearing and length information to use for the arrow, and all this information is inserted into the controls of the dockable window. We then added some additional controls to allow the style of the arrow to be controlled (to an extent).

Rotating the arrow in 3 dimensions

We now had the tools to repeatedly and easily create the raw parameter data that we required, so it was on to the main challenge, that of actually creating a 3D arrow. We thought for a while and then looked at the AGX SDK developer help. The Rotate method of the GeometryOperations class looked like it could help us with the main problem, that of rotating our vertical arrow in both the horizontal and the vertical planes, if we indulged in a little light trickery. The Rotate method rotates XYZ coordinates around a rotation axis created by extending the supplied rotation point in the Z-dimension. This means that all the Z-values remain the same.

The horizontal (XY) rotation for the arrow was easy, we just needed to process all of the points in our arrow using the Rotate method to rotate them by the specified angle around the target point. But for the other axes, we had to translate the XYZ coordinates before they could be rotated using the Rotate method.

Projecting the arrow coordinates

The target coordinate (XY) was captured in Lat-Longs, and the arrow length/height (Z) defined in metres, so any translation of these values first required us to convert all coordinate values to the same units (preferably metres). Fortunately AGX’s SDK comes with coordinate conversion capabilities. All we needed to do was work out a suitable coordinate reference system (CRS), with units in metres, then transform the source points for our arrow into this CRS. We decided that we’d work out the appropriate UTM zone for the end point and use that (further consideration indicated that we needed to tweak this to use polar CRSs for latitudes above +80 and below -80).

Having implemented this code, we moved on to working out the translations required:

To rotate around the Z axis:
- no translation
To rotate around the Y axis:
- X → X, Z → Y, 0 → Z
To rotate around the X-axis:
- Y → X, Z → Y, 0 → Z

Having rotated the values, we translated them back, incorporating the original omitted value:

For objects rotated around the Z-axis:
- Rotated X → X, Rotated Y → Y, Original Z → Z
For objects rotated around the Y-axis:
- Rotated X → X, Original Y → Y, Rotated Y → Z
For objects rotated around the X-axis:
- Original X → X, Rotated X → Y, Rotated Y → Z

Adding some depth

We now had a rotated (in both the horizontal and vertical planes) arrow pointing at a target location, but its still a flat arrow. In order to give it some depth, we simply rotated the arrow by a small angle perpendicularly away from itself, around the target point, and filled in the gaps that had opened with additional polygons. To make it easier, we actually did this at the start of the process, when the arrow was vertical.

Once we’d got all of the processed coordinate sets, we then back-projected them to Latitude-Longitude and exported them to a KML format file, which we can use in any KML-aware software.

So that’s that, we now have a tool that allows us to create enormous 3D arrows from space. We've made the tool available fo download here (2Mb zip file) so please feel free to give it a spin – the download includes the AGX 900 Add-In (.EAZ file) and a PDF containing instructions on how to install and use the Add-In.

We are working on a few other KML tools and will be blogging about these in the future, so please check back if you’re interested!

Posted by Ross Smail, Head of R&D, Exprodat Consulting Ltd.

Who Owns Your GIS?

Thu, 15 Oct 2009

It’s become clear to me through a number of engagements with E&P companies that ownership of GIS is falling between the cracks. GIS as a distinct ‘practice’ in the E&P business is a relatively recent development, certainly for many small to mid-size companies. What started out as a niche application championed by a few ‘techies’ in Exploration has quickly become a burgeoning domain in its own right.

GIS is different from many other technologies. It delivers a horizontal applications platform applicable across the entire lifecycle of the Petroleum sector. Unlike other generic technologies deployed by IT groups, there is also a distinct science (Geomatics) that needs to be understood to properly exploit the potential of the technology.

The ‘spatial data wave’ is hitting E&P companies now in the same way that well and seismic did a decade or more ago, but the data is not as easy to classify as those data types: it spans the business. Spatial data is more than files on disk managed by IT groups, and requires an understanding of Geomatics and GIS technology to manage it effectively.

So who owns and manages GIS in your company? Is it the E&P data management group? The IT group? Or the Geomatics group (if there is one)? And just as importantly who owns the strategy for developing GIS going forward? One of the support groups, or perhaps even someone in the business? Who is responsible for making sure that the user of GIS systems has an appropriate understanding of the underlying science? In my experience, its rare that someone sticks their hand up and says ‘that’s mine’, although many groups tinker around the edges, assuming someone else has control of the ‘big picture’. Quite often no-one actually does!

I would say that without clear ownership and a coherent strategy, companies do not realise the full value of GIS. In a recent report by IHS CERA, presented recently at the 2009 ESRI European PUG conference in Norway, they observed that ‘unexpectedly high deployment costs combined with the difficulty in quantifying GIS value have reduced enthusiasm and sponsorship on the part of senior management.’ Perhaps the issue of ownership is the key to addressing this problem. Without ownership and strong governance, costs will escalate and potential benefits will not be realised, measured and communicated.

Posted by Gareth Smith, Managing Director, Exprodat Consulting Ltd.

Locating Sites for Offshore Wind Farms

Thu, 08 Oct 2009

In the past four decades the United Kingdom Continental Shelf (UKCS) has proven to be a major area for hydrocarbon exploration and production. However, as governments across the globe focus more on addressing climate change, energy production must in turn encompass more renewable technologies. The EU target to source 20% of Europe’s energy from renewables by 2020 is ambitious, and offshore wind energy is expected to provide a large share of the UK’s contribution.

The UK government therefore is undertaking two key activities that will contribute to delivering a further 25 GW from offshore windfarms: (1) the government’s UK offshore energy SEA; and (2) The Crown Estate’s Round 3 programme, in which the Crown Estate’s role will revolve around programme delivery, and zonal contract management, i.e. working with partners to indentify suitable windfarm sites within the zones on offer.

At Exprodat we wondered whether we’d be able to employ our Team-GIS Acreage Analyst software (normally used for rapidly evaluating and grading petroleum licenses and locations) to analyse and rank the proposed Round 3 locations.

Data Inputs

Some of the key factors for locating offshore wind farm developments, as noted by the SEA, are wind speed, water depth, proximity to areas of high electricity demand and availability of connection points to onshore transmission. Team-GIS Acreage Analyst is able to use any ArcGIS Desktop supported vector (point/line/polygon) or raster format as inputs, so data was sourced in spatially-ready formats. Data was obtained from a multitude of sources (e.g. JNCC, DECC, English Hertiage) for input into the location ranking process, including the Renewables Atlas, which provides maps of the distribution of renewable energy resource factors, i.e. wind, wave and tidal, based on the Met Office Numerical Weather Prediction Model (NWP).

Information and datasets provided for the Oil and Gas sector (e.g. pipelines data, surface infrastructure and subsurface infrastructure) were also compiled for use as inputs into the analysis.

Region of Assessment - Holderness

Due to the relative abundance of datasets available in the area, we decided to focus on the Holderness Round 3 region for our proof-of-concept analysis (the purple polygon in the middle of the map below).

The table below describes the input datasets, any queries or filters applied to them and the analysis types applied (these layers are also shown in the map above).

Part of the advanced functionality of Team-GIS Acreage Analyst is the optional ability to sub-divide the 'base layer' (i.e. the layer containing the locations that you wish to rank) polygon areas into an equally spaced grid and then carry out the analysis on each of the resulting cells. The Holderness Round 3 covers approximately 160 km x 40 km, and was sub-divided on-the-fly by the tools into 845 equal 2.5 km x 2.5 km grid cells.

Analysis and Scoring

The location ranking schema used in the analysis was based on general assumptions (potentially erroneous, but good enough for our ‘proof-of-concept’) that the optimal locations were close to existing infrastructure (pipelines, platforms, etc.), did not contain protected wrecks, had specific sea-bed geology types (see table above), and high potential wind energy values. Each input layer was then spatially analysed with respect to each grid cell in the base layer (the Round 3 Holderness region), and scored based on the results.

The ‘Wind Potential 100m (RA)’ layer (power at 100m, calculated per square metre of rotor swept area) was given a weighting of 2 as it was perceived as the most important in the analysis, and then scored using the following classification:

The Seabed Landscape data was analysed by assessing percentage overlap of suitable geology type with each grid cell, while proximity analyses were applied to the Wrecks and Infrastructure layers. Scoring schemas for these layers were then applied in accordance with the assumptions of the analysis.

Results

The results of the analysis are shown below. The analysis, once the initial data had been gathered, took only a minute or so to complete. Using Team-GIS Acreage Analysis we were then able to iterate the analysis and scoring by re-loading parameters from previous runs, tweaking the set-up, and re-executing. This enabled us to rapidly analyse some of the sensitivities within the data.

Clearly our analysis is not perfect, and further enhancements to the ‘proof-of-concept’ should include integration of additional data, e.g. shipping lanes, radar data, cable network, connection points, fishing areas, water depth and economic factors, etc., as well as correcting our initial assumptions. However, it does show that Team-GIS Acreage Analyst has the ability to rank potential offshore wind farm locations based on multiple and complex datasets, without the analyst having to build a bespoke Geoprocessing model.

Posted by Adam Smith (GIS Consultant) and Chris Jepps (Technical Director), Exprodat Consulting Ltd.

Tip 2: Tabular XY Data

Fri, 02 Oct 2009

The second in our series of Petroleum GIS Tips where members of our training team explain how to perform a common E&P task or petroleum GIS workflow using ArcGIS Desktop. Its the sort of thing our consultants do a lot of when preparing data for our oil and gas clients, and this same workflow can apply to SQLServer Tables, Oracle, Access and even CSV files.

Loading Database Tables with XY Coordinates

Lets imagine that you've been provided with a database of wells for use in a data room and you'd like to import the locations in to ArcGIS Desktop in order to compare their locations with the data in your own company's GIS 'Wells' layer. The database has a well header table and luckily this includes fields for X and Y location. Fortunately the fields are formatted as the numeric data type - if they weren't you'd have to convert them from text into numeric, but that's for another day. To load the data in to ArcGIS Desktop you'll need to follow this workflow:

Open the Add XY Data Tool by selecting 'Tools > Add XY Data' from the ArcMap menu.
Navigate to and select your database and table.
Select the table fields that have the X and Y (or Latitude and Longitude) coordinates.
Select the coordinate system that the wells are in.
Click OK.
A dynamic 'Events' layer will then be added to the table of contents.
You can then export this data to a shapefile or other feature class by right clicking on the file in the table of contents and select 'Data > Export Data'.

Posted by Chris Jepps, Technical Director, Exprodat Consulting Ltd.

Risk Segments: Vectors 3, Rasters 1

Thu, 01 Oct 2009

A lot of petroleum GIS conference presentations I've seen about play fairway mapping describe processes whereby the common risk segment layers are produced in raster format. I guess this is due being able to use Spatial Analyst and the Raster Calculator to 'stack up' layers with numeric attributes and perform mathematic calculations on them. That's all well and good, but there are significant advantages to be had in keeping the analysis ouputs in vector format, and not turning everything into a grid.

1-0

First up we have data resolution and accuracy. If you derived your risk layer from proxies such as GDE maps or even hand drawn interpretation, then imposing a grid upon the data is going to reduce resolution and potentially introduce errors. Its the same principal as why a 2 megapixel camera probably takes 'worse' pictures than a camera using film. This can be seen faily clearly in the examples below - note that the western well is incorrectly located inside the lower scoring (orange) segment on the raster representation (left image), when it should sit outside of it (right image).

2-0

Secondly, keeping the analysis within the vector domain means it is possible to maintain an audit trail within the risk layers, using attribution. Consider the two example 'Identify' tool queries shown below. The first shows the results of an 'Identify' query performed on a raster format risk segment layer at a location of interest. We can see that the raster layer represents the common risk segment 'sum' value for the Jurassic Play, and that the location queried has a risk value of 1.8.

Compare this to the following query performed on a vector layer at the same location. We can see that this Upper Jurasic Play common risk segment layer has been derived from 3 component risk layers - Reservoir Presence, Seal Presence and Hydrocarbon Charge. In vector format the user can view each component risk value, as well as a number of 'summary' risk values (Sum, Product, Minimum, Maximum and Mean).

We can therefore see that the vector layer provides far more information to the user than the raster layer.

3-0

An extension of the attribution argument - keeping things in the vector domain allows you to label them. So, you can add simple labels based on one of the attributes, e.g. risk sum (as shown above), or you can add more complex labels that report, say, the final risk value and the highest risked element and its risk value, to help illustrate why a particular segment is risked as it is. So for our segment of interest, the user can see immediately that the risk sum value is 1.8, and that the segment scores lower than its neighbours for overall risk because the highest risked factor (Seal Presence) has scored 0, due to the seal being interpreted as being absent.

3-1

That's why Exprodat's Team-GIS Segment Analyst tools use the vector domain for most analyses. I say 'most', the user is able to optionally produce raster outputs in the final phase, when the combined common risk segments are created, as these can be useful for calculating zonal statistics. Hey, we're not anti-raster here you know!

Posted by Chris Jepps, Technical Director, Exprodat.

Zen and the Art of Web Mapping

Wed, 30 Sep 2009

When I first started working with web-based GIS, oooh, back in 2001 or so, it seemed that ESRI was attempting to deliver "desktop-GIS-in-a-browser", and as we know now, generally coming up short. Since the birth of ArcIMS, ESRI invested heavily in ArcObjects and ArcGIS Server, presumably to deliver on the aims of its original internet mapping vision.

However, new web programming paradigms (hello Silverlight, Flex et al) coupled with changing expectations of what a web-based map should look like (hello Google Earth and Bing Maps [née Microsoft Virtual Earth]) have led ESRI to revise its vision. A presentation entitled ' Bringing your geographic information to life ' by Clint Brown (ESRI's Director of Software Products) at ESRI's 2009 User Conference summarised this nicely.

Whilst Brown's statement that "There is a new kind of map – a 'Web Map'" might annoy those of us who have spent hours, days, or weeks of our lives hacking away at an ArcIMS AXL file (like, duh, what we doing before [dribble dribble]?), its at least a hook on which to hang a reframed picture. Brown goes on to explain the new 'Elements of a Web Map' as being limited to:

1. Multi-Scale Base Maps

'One or more maps that provide a framework or context for displaying the operational layers': In a nutshell, the base map, but sourced from a combination (mash-up?) of map services from multiple sources (e.g. ArcGIS Online etc.) and configured with settings such as scale dependency, symbology and labelling optimised for use at mutliple scales, designed specifically for what the 'web map' will ultimately be used for. End users won't have to worry about controlling the base map layers, they'll just behave exactly as the users expect. In fact, I'd say that the intention should be that the base map layers do their job so well as to go almost unnoticed by the user. Here's an ESRI Geology map example - the base map layers are the Reference and Terrain layers.

2. Operational Layers

'The focused set of layers that users work with': The operational layers are the key content of the map, and they should be built in such a way as to allow the user to interogate the results in order to achieve specific analytical goals (see below). ESRI recommends that the operational layers are also 'multi-scale', and displayed as an individual map layer sourced from a map service with mutliple layers, using client-side graphics. In our Geology map example the operational layers are the Geology layers.

3. Focussed Tools for Interogating Operational Layers

'Information Popups and Reports for Operational Layers': The functionality around which the map is built, or, to put it another way, the map's 'raison d'etre'. Out are the days when web maps are there just to let you see your data. In come highly functional maps that non-GIS savvy users can drive, harnessing all the power of ArcObjects and geoprocessing in order to interogate, analyse and run reports on the operational layer, with all the compexities hidden from the user. So, no table of contents, no 'active layer', no printing widget, no complicated menus or toolbars. Unless of course the operational layers require it. In short, maps that a dummy, an idiot, could use - someone who has never even heard of ArcGIS Desktop, let alone waited for the 'Add Data' dialog to appear. In our Geology map example there is one operational tool - the opacity slider in the lower right corner that changes the transparency of the Geology.

Whether we'll ever replace the geoscientists 'data index map' that have become common in the E&P industry with new-style 'Web Maps' (called "Rich Internet Applications") remains to be seen, but its an interesting vision, and certainly one I can see applications for in the E&P sector, e.g. with management dashboards or interpretation summary and reporting tools.

But I think its somewhat ironic that with ArcGIS Server (from 9.3 onwards) finally technically able to deliver on the promise of "desktop-GIS-in-a-browser" its unlikely that we'll be seeing 'all singing all dancing' web-based map tools widely delivered as desktop GIS replacements anytime soon.

Its all going very Zen: less complexity, fewer buttons.

Posted by Chris Jepps, Technical Director, Exprodat Consulting Ltd.

Using GIS to Rank UKCS Blocks

Tue, 29 Sep 2009

With the UK's 26th Licensing Round expected to be announced in January 2010 its likely that the thoughts of the petroleum companies operating in the North Sea will soon turn to figuring out what the best acreage likely to be on offer is.

Coincidentally, Nursultan Yakhiyayev, one of our 2009 summer students from the Imperial College Petroleum Geoscience MSc has recently spent a couple of months doing just that - for the UKCS Central North area, using a combination of desktop GIS tools. The project aims were to perform a regional-scale block ranking of the UKCS CNS based on an analysis of the key play risks, and in turn to analyse the acreage positions of the companies active within the area.

For the purposes of the project Central North Sea Palaeozoic to Late Eocene plays were subdivided into three major play groups according to three main rift related phases. Twelve main plays were described, and exploration risk maps developed for each play reflect the presence of prospective play fairways in the study area and future exploration opportunities.

This was achieved by constructing a set of common risk segment maps (using Team-GIS Segment Analyst) for the twelve petroleum plays identified across the area. For each play, risk maps were developed for each petroleum system element (i.e. reservoir presence, seal presence and hydrocarbon charge) using paleogeographic/depositional environment datasets as 'proxies' (specifically Exploration Geoscience's What Map and the Millennium Atlas GIS Edition datasets).

Stacking the 12 plays and summarising the data by block then allowed the licensed acreage to be ranked according to user defined scoring and weighting criteria (using Team-GIS Acreage Analyst). Then, by integrating company equity data for the blocks, companies active within the area were able to be analyzed, compared and ranked, with the results showing which companies have the best acreage.

For a summary of the project's methodology and results, please download Nursultan's MSc presentation (requires login or registration).

Posted by Chris Jepps, Technical Director, Exprodat.

Exploratory Spatial Data Analysis 1

Wed, 23 Sep 2009

Exploratory Spatial Data Analysis (ESDA) is a group of techniques used to describe and visualize spatial distributions, to highlight patterns affecting the distribution of observations and to identify outlier values. Each interpolation technique has its own advantages and disadvantages, depending on the characteristics of the input dataset. ESDA techniques provide the GIS user with guidance on how to decide which interpolation method to use when gridding and contouring data.

Earlier this year we released a white paper entitled "Exploratory Spatial Data Analysis - Optimizing Interpolation of E&P Datasets" for use by Petroleum exploration and production (E&P) sector geoscience and GIS professionals. The paper, first presented at ESRI’s 2009 Petroleum User Group (PUG) Conference (www.esri.com/events/pug/) in Houston, Texas, can be downloaded for free from our Members page, and explains techniques for selecting suitable interpolation methods for creating accurate surface models using Geographical Information System (GIS) technology.

Since releasing the paper our internal R&D team has been working up the results and taking them on to the next level. We're hopeful that we'll have the results of Part 2 to present before the year is out.

If you'd like to keep up to date with this and other news and developments from Exprodat, please take a look at our RSS Feeds.

Posted by Chris Jepps, Technical Director, Exprodat.

Tip 1: Georeferencing

Tue, 15 Sep 2009

Welcome to the first of our Petroleum GIS Tips series, where members of our training team will explain how to perform a common E&P task or petroleum GIS workflow using ArcGIS Desktop. First up is an old chestnut - georeferencing an image.

Georeferencing Images

One day you are lurking by the coffee machine discussing your next Fantasy Football transfers with a colleague, when your Exploration Manager bounces up to you explaining that he has a 54Mb PowerPoint file full of maps and images exported from SeisWorks that he'd like you to 'load into GIS' so that he can view the data in context with your organisation's GIS-based play fairway data. No problemo, you think, and totter off to get Dave, the GIS technician. After fifteen minutes of looking you realise that Dave has gone off to one of those pesky, yet cushy, GIS conferences and isn't back until next Monday. You instantly regret not bothering to take notes when Dave last showed you how to do it...

Not to worry - our first training tip is here to help:

Add the image to your ArcMap document.
The image will not need a spatial reference (SR) as long as the correct SR is being used in the ArcMap data frame.
To check the SR click on View from the ArcMap top menu bar and select Data Frame Properties.
Select the Coordinate System tab: under the “Select Coordinate System” you should see the projection highlighted.
If “Unknown”, you need to change this by clicking on the plus icon next to “Predefined”, selecting the appropriate projection and clicking Apply.
Open the Georeferencing toolbar by checking on the toolbar name in View > Toolbars.
Select Fit To Display in the Georeferencing drop down menu.
You should now have the image in your view; to make it easier to georeference you can click the shift/rotate icons and move the image on the map to get the image closer to where it should be in reference to the other layers.
Click ‘View link table’ this contains all of the control points when you start georeferencing and also shows the RMS error of the control points.
Note: The RMS (root mean square) is a measure of how consistent the transformation is between the control points. RMS is a good assessment of the accuracy of the transformations, though don’t confuse a low RMS error with an accurate transformation as it may still contain errors due to the control point be entered incorrectly. RMS is best as a quick assessment but essentially you should quality check all your georeferenced imagery in order to ensure it is projected correctly and not distorted.
Click on the add control points icon.
Click on the image at the location of the point to be georeferenced.
Click on the map where that point should actually be.
The image will now automatically project itself on the basis of that control point. The auto-adjust can be turned off in the georeferencing drop down menu. Click ‘update display’ once all your control points have been entered if you turn this off.
A spread of control points is needed over the whole extent of the image for an accurate projection for best results.
Once finished, quality check for any distortion in the image, if there is you will need to check your control points are correct. Control points can be removed by selecting them in the link table and then clicking the 'X' button.
These control points can also be saved and loaded for other images of the same area and size in the link table.
Finally, select Update Georeferencing from the georeferencing drop down menu.

To learn more about the above, and loads of similarly useful workflows, you could take a look at our GIS training programme, which has been spefically tailored for the oil and gas industry. Or, for further information on the above process, check out the ArcGIS Desktop online help.

Posted by Chris Jepps, Technical Director, Exprodat.

The Investment that Keeps on Giving

Mon, 14 Sep 2009

At Exprodat we often have to communicate the value of training, especially when times are hard (hello credit crunch). A while ago I came across an interesting blog post on the ESRI Training Matters website which summed this up nicely. It being some months since it was posted, and ESRI being enthusiastic bloggers, it has now long disappeared from the first few pages of blog posts, so I thought it might be useful to re-post some of it here.

I think the most interesting thing is the way it highlights 4 key benefits of keeping your training investment going, and these are posted here pretty much verbatim...

Increase Staff Efficiency

Training increases staff efficiency—it's not uncommon to hear students who have learned a single software task in an instructor-led class excitedly claim they will now save hours on the job.

Increase Staff Productivity

Training increases staff productivity—a natural result of increased efficiency, productivity increases when tasks are completed more quickly. More tasks can be completed in less time.

Increase Staff Knowledge

Training increases staff knowledge—this may seem obvious, but knowledge is more than the sum of the topics covered in a class. Knowledge is the synthesis of different concepts and skills learned over time, which enables a person to recognize and act to prevent errors and reduce liabilities.

Find New Opportunities

Training leads to new business opportunities—again, a natural extension of the previous benefit. When staff are well trained, they are freed to be creative and see possibilities for information products and workflows that may not have been apparent before the training. Interacting with peers in class by exchanging ideas and experiences certainly helps realize this benefit.

If you're interested in reading the full article, it was posted by Suzanne Boden, on Dec 2nd 2008, on ESRI Training Matters.

Posted by Chris Jepps, Technical Director, Exprodat.