Integrated CNN–LSTM–XGBoost hybrid model predicts shale oil seismic attributes and global oil price trends

Abstract

Abstract This study proposes a hybrid CNN–LSTM–XGBoost model that integrates shale oil seismic attributes with macroeconomic indicators to predict global oil prices. The model extracts spatial features from seismic volumes using 3D CNNs and captures temporal dependencies in economic data via LSTM, with fused features processed by XGBoost regression. It achieves an RMSE of 3.61 USD/barrel, R 2 of 0.847, and MAPE of 6.98%, outperforming standalone models by 12.7–23.1%. SHAP analysis shows that seismic attributes contribute an average marginal impact of 0.19 on the model output. The model uses seismic attributes as input features to forecast oil price trends, providing a transparent and interpretable framework for integrated geophysical–economic analysis. At the local scale, the model assists shale oil operators in optimizing drilling and production decisions by linking seismic-derived reservoir quality to medium-term supply expectations. At the broader global scale, the framework demonstrates how geological information can be systematically incorporated into energy market forecasting, offering a new paradigm for geoeconomic modeling under supply‑side uncertainties.