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Keywords
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Water saturation (Sw), Petrophysical analysis, Machine learning (ML), Outlier detection, Hydrocarbon reservoir, Oil recovery optimization
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Abstract
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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.
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