Machine Learning in Rock Facies Classification: An Application of XGBoost

Abstract

Summary Big data analysis has drawn much attention across different industries. Geoscientists, meanwhile, have been doing analysis with voluminous data for many years, without even bragging how big it is. In this paper, we present an application of machine learning, to be more specific, the gradient boosting method, in Rock Facies Classification based on certain geological features and constrains. Gradient boosting is a both popular and effective approach in classification, which produces a prediction model in an ensemble of weak models, typically decision trees. The key for gradient boosting to work successfully lies in introducing a customized objective function and tuning the parameters iteratively based on cross-validation. Our model achieves a rather high F1 score in evaluating two test wells data.