An online prediction method for rate of penetration based on EWMA-LightGBM-Huber

Abstract:

Rate of penetration （ROP） is a critical indicator for evaluating drilling efficiency and downhole drilling conditions. Accurate ROP prediction is essential for improving intelligent drilling. However， the coupled effects of formation properties， drilling parameters， and operational variations make it challenging for conventional models to simultaneously capture long-term trends and short-term fluctuations， and online prediction is prone to future information leakage. To address this issue， an online EWMA-LightGBM-Huber model based on trend-residual decomposition is proposed for ROP prediction. First， a strict one-sided processing strategy is adopted to perform outlier handling and noise reduction on drilling data， thereby avoiding future information leakage. The exponentially weighted moving average （EWMA） method is then employed to extract the local trend component of ROP. Subsequently， drilling parameters and historical ROP features are integrated， and LightGBM is used to learn the residual component between the actual ROP and the extracted trend. The Huber loss function is further introduced to improve model robustness against abnormal fluctuations. Finally， a sliding-window updating strategy is incorporated to achieve online model updating while drilling and continuous depth-wise prediction. The results demonstrate that the proposed model achieves an R² of 0.9333， with the RMSE reduced by 8.72% and the MAE reduced by 11.92% compared with the best-performing benchmark model RF， exhibiting superior prediction accuracy and stability. Ablation experiments verify the effectiveness of trend extraction， residual learning， and robust optimization modules. Furthermore， sliding-window analysis determines appropriate parameters for online updating. This study provides an effective data-driven framework for real-time ROP prediction under complex drilling conditions.