Machine learning in geoscience: facies classification with features engineering, clustering, and gradient boosting trees
Facies classification is the process to determine the local rocks lithology by analyzing indirect measurements, such as well logs. Usually it is done manually by an interpreter. In this work, I am presenting an automatic method for facies classification by the use of feature engineering and gradient boosting trees. I used a set of classified well logs to train a multiclass machine learning model, and compared the predictions with both raw and processed features in a blind well. I could demonstrate that preparing the, by creating new features from the original well logs, such as their gradients, polar coordinates transformations, and clustering analysis, increased the predictions accuracy from 47% to 60%.