Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning

Accurate estimation of water saturation (Sw) is essential for optimizing oil recovery strategies and is a key component in petrophysical analyses of hydrocarbon reservoirs. Traditional Sw estimation approaches often face limitations due to idealized assumptions, dependency on core-derived parameters, and geological heterogeneity. In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. Nine well log parameters-Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature-were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. To improve model performance, a Gaussian outlier removal technique was applied to eliminate anomalous data points. The models were rigorously validated using multiple training/testing data splits and ten independent runs to ensure statistical reliability. Among the tested models, SVM achieved the highest accuracy, with R² values of 0.9952 (test) and 0.9962 (train) and RMSE values of 0.002 (test) and 0.001 (train). These results demonstrate that ML-particularly SVM-offers a robust and accurate alternative for Sw estimation, supporting more effective reservoir evaluation and oil recovery optimization.