Hybrid optimization of interpretable ensemble machine learning for petrophysical property prediction from well logs

The precise prediction of petrophysical properties in tight reservoirs is essential for accurate reservoir characterization but remains impeded by significant lithological heterogeneity and complex, nonlinear relationships among well-log features. To address this, we propose a robust and interpretable machine learning framework that synergizes a stacked ensemble architecture with a post hoc physics-informed refinement step for predicting porosity and water saturation. The methodology employs a multi-stage process: (1) model-specific recursive feature elimination with cross-validation (RFECV) to identify optimal feature subsets; (2) a hybrid Genetic Algorithm–Particle Swarm Optimization (GA–PSO) strategy for efficient hyperparameter tuning; and (3) a stacked ensemble integrating Random Forest (RF), LightGBM, and CatBoost, with a Ridge regression meta-learner. We evaluate two configurations: hyperparameter optimization alone (Hybrid_Hyper_XGB) and joint optimization of hyperparameters and stacking weights (Stacked_Hybrid_Full). The superior Stacked_Hybrid_Full model is further enhanced by a post hoc physics-based refinement, where priors derived from the Wyllie time-average equation augmented with density-neutron crossplots and the Archie-Simandoux model are blended as soft regularizers, ensuring geological consistency without retraining. Comprehensive validation demonstrates that the physics-informed Stacked_Hybrid_Full model achieves superior performance, with R 2 values exceeding 0.91 for porosity and 0.83 for water saturation. Depth-resolved analysis confirms a significant reduction in prediction error and improved capture of structural features, particularly within laminated and low-porosity intervals. Model interpretability, probed via SHapley Additive exPlanations (SHAP), identifies permeability, resistivity, gamma ray, and shear velocity as the dominant predictive features and elucidates nontrivial interaction effects aligned with petrophysical principles. This work presents a transferable workflow that successfully bridges data-driven prediction with physical plausibility. The framework significantly enhances predictive robustness and model transparency for petrophysical characterization in heterogeneous tight reservoirs, offering substantial practical utility for reservoir evaluation in unconventional plays.