Interpretable machine learning for predicting EUR of shale gas wells in the Weiyuan block

sub-member > drilled length of type I reservoir > 360-day flowback rate > fracturing fluid volume > vertical depth. The first four features contribute positively to the model output to different degrees, while vertical depth has a more complex effect on the model in this study, not showing a simple positive or negative trend. The Optuna-GBDT model, combined with SHAP-based interpretation, provides a practical and efficient idea for EUR prediction in shale gas wells.