Abstract

Accurate water saturation estimation in deep shale reservoirs is complicated by clay-related additional conductivity and coupled pore, organic-matter, and structural effects. This study develops a feature-level physics-guided machine-learning framework, termed PhysML-Hybrid. Five mechanism-derived descriptor groups representing clay–water interfacial behavior, low-resistivity correction, pore connectivity, organic-pore development, and structural stress were integrated with conventional reservoir variables in a validation-weighted ensemble of random forest, XGBoost, and Bayesian neural network models. The framework was evaluated using 153 depth-matched samples from five wells in the Dingshan area of the Sichuan Basin. The data were divided into 107 training, 16 validation, and 30 independent test samples, and target-stratified five-fold cross-validation was conducted exclusively within the training set. Mean cross-validation R2, MAE, and RMSE were 0.907 ± 0.009, 1.69% ± 0.10%, and 2.25% ± 0.14%, respectively. On the independent test set, the corresponding values were 0.902, 1.77%, and 2.34%. PhysML-Hybrid outperformed Archie, SVM, ML-only, and Phy-XGB. SHAP and statistical analyses identified clay content, the curvature–clay interaction, TOC, pore connectivity, and structural descriptors as influential variables; candidate transitions were interpreted as dataset-specific rather than universal thresholds or causal relationships. Three blind-well cases provided supplementary evidence of cross-well applicability, although larger independent multi-basin datasets are required to assess transferability.

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