Presentation, demo, and Q&A with Brendon Hall, Energy Solutions Group, Enthought
- Oil and gas industry professionals who are looking for ways to extract more value from expensive science wells
- Those interested in learning how artificial intelligence and machine learning techniques can be applied to core analysis
You Will Learn:
- How core CT data, photographs, well logs, borehole images, and more can be integrated into a digital core workshop
- How digital core data can shorten core description timelines and deliver business results faster
- How new features can be extracted from digital core data using artificial intelligence
- Novel workflows that leverage these features, such as identifying parasequences and strategies for determining net pay
Geoscientists and petroleum engineers rely on accurate core measurements to characterize reservoirs, develop drilling plans and de-risk play assessments. Whole-core CT scans are now routinely performed on extracted well cores, however the data produced from these scans are difficult to visualize and integrate with other measurements.
Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core.
In this webinar and demo, we’ll start by introducing the Clear Core processing pipeline, which automatically removes unwanted artifacts (such as tubing) from the CT image. We’ll then show how the machine learning capabilities in Virtual Core can be used to describe the core, extracting features such as bedding planes and dip angle. Finally, we’ll show how the data can be viewed and analyzed alongside other core data, such as photographs, wellbore images, well logs, plug measurements, and more.