基于EWMA-LightGBM-Huber的机械钻速在线预测方法

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

摘要:

机械钻速是表征钻井效率与井下钻进状态的关键参数，其准确预测对提升钻井智能化水平具有重要意义。受地层条件、钻井参数及工况变化等因素耦合影响，传统模型难以兼顾整体趋势与局部响应，在线预测易存在未来信息泄露风险。针对上述问题，本文提出一种基于趋势-残差分解的EWMA-LightGBM-Huber机械钻速在线预测模型。首先，采用严格单侧处理策略对钻井数据进行异常值处理与降噪，避免未来信息泄露，再采用指数加权移动平均法提取机械钻速局部变化趋势；随后融合钻井参数与历史机械钻速特征，利用LightGBM学习趋势残差，并引入Huber损失增强模型鲁棒性；最后结合滑动窗口更新策略实现模型的随钻在线更新与连续深度预测。结果表明，所提出模型的R²达0.9333，较最优对比模型RF的RMSE降低8.72%、MAE降低11.92%，表现出更优的预测精度和稳定性，消融实验验证了趋势提取、残差学习和鲁棒优化模块的有效性，滑动窗口分析进一步确定了模型在线更新的合理参数。研究结果为复杂钻进条件下机械钻速实时预测提供了一种有效的数据驱动方法。

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.