A Two-Stage Hybrid Intelligent Model with Domain-Informed Composite Features and SHAP-Based Interpretation for Stratigraphic Identification

Accurate stratigraphic identification is essential for petroleum-development planning, but class imbalance and similar well-log responses between adjacent strata remain major challenges. This study uses well-log data from nine anonymized wells in the Ordos Basin, covering approximately 1,200–2,400 m and nine stratigraphic units from Yan 9 to Chang 10_1. Seven wells were used for training and hyperparameter optimization, while Wells H and I were reserved for blind evaluation. A two-stage framework was developed by decomposing the nine-class task into three-class and seven-class subtasks using an operational depth constraint within Yan 10_2. Savitzky–Golay filtering was applied, and XGBoost, CatBoost, LightGBM, Stacking, and Att-BiLSTM were evaluated. Three composite features were constructed before SHAP analysis based on petrophysical relationships and exploratory analysis of the training wells. SHAP was used for post-hoc interpretation rather than feature construction or selection. CatBoost achieved the strongest overall performance among the evaluated models, although no single model performed best for every stratum. With an adjacent-stratum boundary tolerance of approximately ±1 m, the framework achieved accuracies of 93.4% and 95.1% for Wells H and I, respectively, with macro-F1 scores of 0.867 and 0.835. These results demonstrate its potential for stratigraphic identification within the study area.