The evaluation of petroleum systems via basin modeling, mapping, geochemical analyses, etc., is now a mature field due to codification of the petroleum system concept coupled with the development of sophisticated basin modeling software. Even so, evaluating petroleum systems with numerical techniques remains primarily a deterministic process resulting in non-unique solutions. In this talk, both synthetic analyses and real world case studies illustrate how machine learning is deployed in exploration workflows to reduce uncertainty. These examples demonstrate the mathematical and scientific reasoning for workflow design and the challenges encountered. The main goal of this approach is to demystify machine learning by showing how it can be effectively used in an exploration context if domain experts work together to integrate results. In this way, the value of machine learning in the evaluation of Earth resources enhances geologic understanding, one that moves beyond buzzwords and proprietary algorithms.
Speaker: Allegra Hosford Scheirer, Stanford
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Stanford, CA 94305
Website: Click to Visit