Explainable Intelligent Framework for Reliable Drilling Rate Prediction and Feature Selection in Complex Sedimentary Formations

Accurate prediction of the drilling rate of penetration (ROP) is crucial to optimizing drilling performance, wellbore stability, enhancing operational efficiency, and reducing the overall cost of hydrocarbon exploration. This study focused on evaluating and comparing different machine learning models for ROP prediction and identifying the most influential operational drilling and gas‐derived parameters affecting ROP performance for clastic sedimentary formation. To achieve these objectives, four intelligent models of bidirectional long short‐term memory (LSTM), random forest (RF), categorical boosting (CTB), and extreme gradient boosting (XGB) are implemented for assessing the combined effect of drilling parameters and gas‐derived indices on ROP prediction. The results demonstrated that the optimized deep learning model with LSTM achieved superior predictive accuracy than the ensemble models of RF, CTB, and XGB, with an excellent coefficient of determination ( R 2 ) of 85%, with a root mean square error (RMSE) value of 2.58 m/h. Interpretable SHapley Additive exPlanations (SHAP) showed that the most dominant factors are hook load and bit depth for ROP estimation. It is found that SHAP‐based contributions of gas‐derived factors and heavy gas index are also significant, exhibiting the robustness and adaptability of LSTM for assessing the combined effect of drilling mechanical parameters and gas‐derived indices on ROP prediction. This study contributes to scientific transparency and operational excellence by developing an interpretable intelligent framework for reliable prediction of subsurface drilling rates in hydrocarbon production. The proposed framework provides reliable, data‐driven insights that empower petroleum geologists and engineers to make safer drilling decisions, enhance operational efficiency, and promote economically sustainable subsurface resource development.