A systematic analysis of machine learning methods for rock wettability prediction: Algorithm performance and computational considerations

Rock wettability fundamentally controls fluid distribution in porous media and directly impacts enhanced oil recovery applications. Traditional laboratory measurements, while accurate, are time-consuming and provide limited spatial resolution, which constrains comprehensive reservoir characterization. Here we present a machine learning approach for rapid wettability prediction from X-ray fluorescence (XRF) elemental data, addressing the need for efficient screening methods in reservoir analysis. We systematically evaluated seven machine learning algorithms combined with four preprocessing strategies on 243 data points from 61 rock samples across diverse lithologies. The most effective approach utilized Extreme Gradient Boosting (XGBoost) with Synthetic Minority Oversampling Technique (SMOTE), achieving 96.7% accuracy on held-out test data. The model demonstrated robust performance across all wettability classes, correctly identifying 100% of strongly water-wet samples, 75% of intermediate-wet samples, and 90% of strongly oil-wet samples. Learning curve and power analyses confirmed dataset adequacy, with statistical power of 0.87 for detecting medium effect sizes. Feature importance analysis identified rubidium, bromine, and arsenic as key elemental indicators of wettability classes. SHAP (SHapley Additive exPlanations) analysis revealed that higher rubidium concentrations are associated with more water-wet behavior, consistent with its large ionic radius modifying surface electrostatics at mineral-fluid interfaces. These findings align with transition metals influencing surface chemistry through oxidation state changes, while also revealing previously unrecognized roles for trace elements in wettability control.