Facies Classification: Combining Domain Knowledge with Machine Learning Solutions

Abstract

Summary Automated facies identification workflows which use Machine Learning (ML) are publicly available but perform sub-optimally (accuracy in the order of 60%) due to a lack of integration with geological domain knowledge. Existing tools consider well log values mostly on a depth-by-depth basis, using only very basic feature engineering. Our solution aims to integrate ML with well-established geoscience principles (also referred to as geo-rules) such as sequence stratigraphy, proximal-distal trends, and log-trend patterns. Geological knowledge is incorporated into ML to improve the quality and robustness of facies prediction and is captured as additional geologically-inspired features added to the dataset. These features include the mean value and other derived properties of intervals, density-neutron separation, segmentation and wavelet transform. All ML algorithms tested with this augmented set of features show significant improvement in performance metrics as compared to solutions with basic logs only.