ArcGIS Image Analysis for Petroleum

Use remotely sensed data in your workflows

This course provides you with a general awareness of remote sensing and teaches you how to access and apply image and terrain data to oil and gas applications using Esri’s ArcGIS for Desktop (ArcView) software. When you complete this course you will have the knowledge and confidence to use remotely sensed data in your workflows and be able to more effectively liaise with remote sensing specialists.

Data collected through the use of remote sensing techniques has many applications within the E&P sector. This course provides you with an awareness of:

• Data availability, sensor capability and remote sensing processes
• Image analysis techniques in ArcGIS for Desktop
• Interpretation & sector application (Geoscience, HSE, Engineering)

Pre-requisites

Participants attending this course should have completed an introductory ArcGIS for Desktop course or already be familiar with ArcGIS for Desktop or equivalent mapping software. Although petroleum exploration and production sector knowledge is not required, this course is geared towards helping participants use remotely sensed data in E&P workflows.

Intended audience

Geoscience, engineering and HSE professionals and support staff who are going to be using remotely sensed data within GIS projects and need to be able to acquire the relevant ArcGIS for Desktop skills and knowledge.

• Focuses on the application of remote sensing techniques to analyse imagery used in the E&P industry
• Uses industry standard ArcGIS for Desktop tools from Esri (ArcMap, ArcCatalog and ArcToolbox) and ArcGIS Spatial Analyst
• One-day intensive course
• Instructor-led hands-on learning by ‘doing’
• Trainer demonstrations and course validation questions

Learning outcomes

1. Describe the key aspects of remote sensing and digital imagery and give examples of how the technology is used in the petroleum industry
2. Outline four techniques used for pre-processing imagery
3. Prepare imagery for analysis and interpretation using image enhancement histograms
4. Identify and display surface/land cover types based on their spectral reflectance
5. Discuss the use of microwave remote sensing and compare various optical image data and their applications
6. Contrast supervised and unsupervised classification methods
7. Implement the Principle Components Analysis tool to better discriminate between land cover types
8. Apply spatial filters to the digital imagery to enhance specific features or patterns in the imagery that may be useful for exploration
9. Operate the data merging tools to generate pan-sharpened imagery which combines colour and black and white imagery at different spatial resolutions
10. Describe how remote sensing and image analysis could be used in a desktop reconnaissance study and combined with other data sources to help target potential exploration areas