Mechanism-Guided and Attention-Enhanced Time-Series Model for Rate of Penetration Prediction in Deep and Ultra-Deep Wells

Accurate prediction of the rate of penetration (ROP) in deep and ultra-deep wells remains a major challenge due to complex downhole conditions and limited real-time data. To address the issues of physical inconsistency and weak generalization in conventional da-ta-driven approaches, this study proposes a mechanism-guided and attention-enhanced deep learning framework. In this framework, drilling physical principles such as energy balance are reformulated into differentiable constraint terms and directly incorporated in-to the loss function of deep neural networks, ensuring that model predictions strictly ad-here to drilling physics. Meanwhile, attention mechanisms are integrated to improve feature selection and temporal modeling: for tree-based models, we investigate their implicit attention to key parameters such as weight on bit (WOB) and torque; for sequential models, we design attention-enhanced architectures (e.g., LSTM and GRU) to capture long-term dependencies among drilling parameters. Validation on 49,284 samples from 11 deep and ultra-deep wells in China (depth range: 1226–8639 m) demonstrates that the synergy between mechanism constraints and attention mechanisms substantially improves ROP prediction accuracy. In blind-well tests, the proposed method achieves a mean absolute percentage error (MAPE) of 9.47% and an R2 of 0.93, significantly outperforming traditional methods under complex deep-well conditions. This study provides reliable intelligent decision support for optimizing deep and ultra-deep well drilling operations. By improving prediction accuracy and enabling real-time anomaly detection, it enhances operational safety and efficiency while reducing drilling risks. The proposed approach offers high practical value for field applications and supports the intelligent development of the oil and gas industry.