基于Attention-LSTM时序模型的机械钻速预测方法

Application of Temporal Modeling Based on Attention-LSTM in Prediction of Mechanical Drilling Speed

针对石油天然气开采成本优化的需求,聚焦钻井作业中提速的关键技术,即机械钻速的预测。传统预测方法通常只考虑瞬时工程参数对机械钻速的影响,而未充分考虑钻井作业的时序性以及机械钻速在时间序列上的相关性。提出一种结合时序特征的机械钻速预测模型,该模型基于Attention-LSTM架构。通过Attention机制,模型有效捕捉了工程参数与机械钻速之间的相关性,并利用LSTM网络提取参数的时序特征,增强了模型对时间依赖性的捕捉能力。实验结果证实,所提模型相较于传统深度神经网络在预测精度上有显著提升。添加的Attention机制进一步提升了模型的解释性、训练效率及预测准确性。采用实际油田钻井数据对提出的方法进行了验证,并与现有几种机械钻速预测模型进行了对比分析,证明了本文方法在准确性、可靠性及解释性方面的优势。

Traditional mechanical drilling speed prediction methods usually only consider the influence of instantaneous engineering parameters on mechanical drilling speed,without fully considering the sequential nature of drilling operations and the correlation of mechanical drilling speed in time series.A mechanical drilling speed prediction model based on the Attention-LSTM architecture with temporal features is proposed in this paper.The model effectively captures the correlation between engineering parameters and mechanical drilling speed through the"Attention"mechanism,and extracts temporal features of the parameters using LSTM network,enhancing the model’s ability to capture temporal dependencies.The experimental results confirm that the proposed model has a significant improvement in prediction accuracy compared to traditional deep neural networks.The added"Attention"mechanism further enhances the interpretability,training efficiency,and prediction accuracy of the model.The proposed mechanical drilling speed prediction model was validated using actual oilfield drilling data and compared with several existing mechanical drilling speed prediction models,demonstrating the advantages of this method in accuracy,reliability,and interpretability.