Unlocking the value of your subsurface image data through machine learning and image analysis
Images are one of the richest subsurface data sources for data curation projects ranging from thin section photomicrographs (small-scale) through to borehole image logs (large-scale).
However, increasing the value of this data source requires considerable time, technology, domain expertise and innovative solutions. In this webinar CGG's Data Hub will unpack three approaches to successful image extraction:
- Microfacies and porosity prediction from thin section photomicrographs
- Lithology/hydrocarbon prediction from core photography
- Stress regime prediction through borehole image log analysis
What to expect:
- Examples of image enrichment through machine learning and image log analysis
- Fully integrated with other subsurface data via dashboard visualisations
- Application to pre-salt Brazil
Grant Cole
Project geoscientist, CGG Data Hub
Grant is a geologist by background specializing in sedimentology. He has been with CGG since 2020 working on multi-interdisciplinary projects and has recently moved to CGG Data Hub, managing projects and providing subject-matter expertise to all stages of subsurface data curation projects.
Ed Jarvis
Technical head, CGG Data Hub
Ed is a geologist with wide ranging experience across the world. His interest in developing more efficient workflows using technology led to a transition to a technical leadership role within CGG Data Hub. Ed works to drive the creation of new data processes but also focus on the QC and application of the outputs, ensuring all data is available for all interpretations.
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