Classifying basin-scale stratigraphic geometries from subsurface formation tops with machine learning
Presented by:
Jesse R. Pisel
Discussion Starts at 12:00 (MT)
Webinar
Abstract
In this talk we present the concepts, code, and data behind a transfer-learning model for classifying basin-scale stratigraphic geometries from subsurface formation tops. Support vector, decision trees, random forests, AdaBoost and K-nearest neighbour classification models are evaluated to support this challenge. Each model is trained on labelled synthetic stratigraphic geometry data generated in Python using observable geologic principles and concepts. Accuracy is measured using a weighted Jaccard similarity coefficient score, and certainty of each prediction is quantified using margin sampling. The random forest classifier has the highest initial accuracy, and the optimal hyperparameters for the model that yield 88.4% accuracy and 72.8% mean certainty via five-fold cross-validation and active learning are documented on a real-world subsurface dataset. The random forest classifier with optimised hyperparameters is then used to make predictions on the real-world subsurface formation tops dataset. The dataset consists of formation tops for the Upper Cretaceous and Palaeocene strata of the Eastern Greater Green River Basin in south-central Wyoming. Results from model predictions include an area of truncation in the Lance Formation across the basin, and an area of onlap and truncation on the nose of the Rock Springs Uplift that previous studies in the region corroborate. It is believed that this model is most useful for guided interpretation and identifying regions that warrant further inquiry by domain experts.
BIO
Jesse Pisel is an assistant professor of practice in computer science at the University of Texas at Austin. Together with Michael Pyrcz, he is training the next generation of subsurface data scientists. Prior to his role at UT Austin, Jesse worked as a data scientist and petroleum-minerals-field geologist across the western US. He holds an undergraduate degree in geology from Western Colorado University, and a PhD in geology from the Colorado School of Mines.
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