Conventional Log-Based Formation Element Prediction for Reservoir Characterization in the Jimusar Shale Oil Reservoir Using a Stacked Ensemble Learning Workflow

The Jimusar shale reservoir is characterized by saline lacustrine mixed sedimentation and strong reservoir heterogeneity, making continuous identification of formation elemental composition challenging. Although elemental capture spectroscopy (ECS) logging provides direct elemental measurements, its high cost and limited deployment restrict its large-scale application. This study investigates the feasibility of predicting ECS-derived elemental compositions from conventional logging data to support continuous reservoir characterization. A dataset comprising 115,668 depth-matched samples from three wells in the Jimusar Sag, Junggar Basin, was used. Conventional logging curves served as input features, while ECS-derived elemental concentrations were used as prediction targets. After data preprocessing and feature enhancement, correlation analysis identified seven relevant logging curves as key input variables. Four regression models—Random Forest, XGBoost, CatBoost, and LightGBM—were evaluated and compared with a stacked ensemble learning model. Model performance was assessed using five-fold cross-validation and multiple metrics, including R², RMSE, MAE, and relative error. The results show that all four individual models achieved satisfactory predictive performance, with R² values generally around 0.8, whereas the stacked ensemble model provided the highest prediction accuracy and stability. Compared with the individual models, the ensemble model improved R² by 2–10%, reduced RMSE by 5–15%, and decreased relative error by 8–15% across different elemental predictions. Among the predicted elements, Fe achieved the highest accuracy, with an R² value of 0.87. As an exploratory engineering application, the predicted elemental compositions were further compared with hydraulic-fracturing response parameters, achieving a conformity rate of 74.8% with fracturing-operation status. These results suggest that predicted elemental data may provide useful auxiliary constraints for fracture-response interpretation and abnormal-risk identification. Nevertheless, further validation using independent well data is required, and the generalizability of the proposed workflow to other wells and lacustrine shale oil systems remains to be further assessed.