Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization

Abstract

Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification. One aspect of this AI approach is the application of population search algorithms to optimise hyperparameters for enhanced prediction performance. For the first time, Bayesian optimization is deployed to determine the optimal learning parameters for more accurate tree and tree ensemble lithology classifiers. The aim is to rely on the ability of Bayesian optimization to consider previous classification results to improve the output of decision and ensemble tree lithology models using well logs as inputs. The proposed Bayesian optimised decision tree (BODT) generated the best classification accuracy of 89.8% as compared to 86.9%, 83.3% and 81.2% for fine, medium and coarse trees. For the ensembled trees, the Bayesian optimised AdaBoost (BO-AdaBoost) classifier generated the highest improved prediction accuracy of 94.2% while Bayesian optimised Bagged (BO-Bagged) and Bayesian optimised RUSBoost (BO-RUSBoost) had a lower accuracy rate of 94.0% and 77.1% respectively. Additionally, the performance of the Bayesian optimised classifiers offered higher reliability when compared with particle swarm optimization-based artificial neural networks (PSO-ANN). Hence, incorporating Bayesian optimization as a hyperparameter search algorithm will improve litholofacies recognition, leading to a higher accuracy rate and thereby provide an improved alternative for intelligent lithology identification.