Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation
This work addresses rock facies classification for the oil and gas industry, offering an incremental improvement in prediction accuracy.
The paper tackled rock facies classification in reservoir characterization by incorporating physics-motivated feature interactions into feature augmentation, resulting in a robust improvement of approximately 5% in F-1 score compared to existing best methods.
With recent progress in algorithms and the availability of massive amounts of computation power, application of machine learning techniques is becoming a hot topic in the oil and gas industry. One of the most promising aspects to apply machine learning to the upstream field is the rock facies classification in reservoir characterization, which is crucial in determining the net pay thickness of reservoirs, thus a definitive factor in drilling decision making process. For complex machine learning tasks like facies classification, feature engineering is often critical. This paper shows the inclusion of physics-motivated feature interaction in feature augmentation can further improve the capability of machine learning in rock facies classification. We demonstrate this approach with the SEG 2016 machine learning contest dataset and the top winning algorithms. The improvement is roboust and can be $\sim5\%$ better than current existing best F-1 score, where F-1 is an evaluation metric used to quantify average prediction accuracy.