Prediction of Water Saturation in Lacustrine Tight Reservoirs of Chang8 in the Central Ordos Basin—Based on the PSO+LightGBM Model

Tight reservoirs are highly heterogeneous, with complex pore-throat structures and varying fluid occurrences. The Archie equation shows a nonlinear relationship, making traditional logging interpretation methods unreliable for accurately predicting water saturation. This paper employs particle swarm optimization (PSO), using Pearson correlation coefficient-based feature selection, to compare the accuracy of three machine learning algorithms: XGBoost, LightGBM, and MERF in predicting water saturation in tight reservoirs. It also applies the SHAP value algorithm to provide a visual and interpretive analysis of the PSO LightGBM model. The research results indicate that the root mean square error (RMSE), coefficient of determination (R2), and accuracy of water saturation (Swa) of the PSO-LightGBM model on the training and test sets are 0.955, 3.087, 91.8%, and 0.89, 5.132, 85.2%, respectively. Interpretability analysis using SHAP values reveals that the five normalized logging parameters—SP, M2R3, DEN, DT, and CN—are the most influential features in the water saturation prediction model. In application examples involving water saturation prediction across eight sections of tight reservoirs in the study area, the PSO–LightGBM, PSO–XGBoost, and PSO–MERF models achieved Swa of 88.9%, 80.3%, and 87.8%, respectively. The results demonstrate that the PSO–LightGBM model is a reliable and efficient method for predicting water saturation, with significant practical potential.