Application of supervised machine learning for predicting and classifying the lithology type of reservoir rock: the case study of Iraq

Abstract

Accurate lithology identification during subsurface exploration is essential for efficient hydrocarbon production, drilling optimization, and reservoir management. This study investigates automated lithology classification using supervised machine learning (ML) to reduce uncertainty and support operational decision-making in hydrocarbon reservoirs. A dataset from Majnoon oil field in Iraq was used, including sonic (DT), gamma ray (GR), resistivity (LLD, LLS), neutron porosity (NPHI), and bulk density (RHOB). Four ML algorithms were evaluated: decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), and neural network (NN). All models achieved high predictive performance. The accuracy values for KNN, SVM, NN, and DT were 98.62%, 99.08%, 99.39%, and 99.69%, respectively. Among these, DT achieved the highest classification performance, with 99.69% accuracy on validation data and 100% on test data. Notably, for the sandstone class, DT reached a precision of 99.7% and a recall of 99.2%, outperforming the other models. Feature importance analysis revealed that depth and GR logs were the most influential predictors. In addition, error analysis was performed using Percent Bias (PBIAS), confirming the minimal deviation of predicted values from observed data across all models. This work offers a high-accuracy, low-cost framework for real-time lithology prediction, providing a novel integration of interpretable ML techniques in a domain traditionally reliant on expert interpretation. Unlike previous efforts, this study quantitatively compares multiple models and highlights class-specific performance, enhancing practical applicability in field operations.