Hybridization of Optimized Supervised Machine Learning Algorithms for Effective Lithology

Abstract

Lithology identification is an important aspect in reservoir characterization with one of its main purpose of well planning and drilling activities. A faster and more effective lithology identification could be obtained from an ensemble of optimized models using voting classifiers. In this study, a voting classifier machine learning model was developed to predict the lithology of different lithologies using an assembly of different classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier, K-Nearest Neighbor, and Multilayer Perceptron (MLP) models. The result of the comparative analysis shows that the implementation of the voting classifier model helped to increase the prediction performance by 1.50% compared to the individual models. Despite a small significance at deployment in real scenario it improves the chances of classifying the lithology.