"The use of machine learning to predict facies in the Duvernay Formation, an unconventional reservoir in the Western Canada Sedimentary Basin"

Elisabeth Rau - Baylor University Geology Department

2019-2020 FWGS Scholarship Recipient

With the rapidly growing and globally expanding inventory of large and complex datasets, i.e., “big data”, machine learning has become a popular data analytics technique within the geoscience community. Here, we evaluate the effectiveness of machine learning in the prediction of facies, facies associations, and reservoir versus non-reservoir rock types in a proven shale reservoir. The Late Devonian Duvernay Formation is a major petroleum source rock in the Western Canada Sedimentary Basin (WCSB) that with recent advances in drilling and completions technology has become a target for exploration and production. Using the Duvernay Formation as a case study, both the benefits and limitations of machine learning derived facies and reservoir quality predictions from wireline logs are evaluated and discussed.

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