西安石油大学学报（自然科学版）2024，Vol.39Issue(5)：85-95,11.DOI:10.3969/j.issn.1673-064X.2024.05.011
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
摘要
Abstract
Traditional mechanical drilling speed prediction methods usually only consider the influence of instantaneous engineering pa-rameters on mechanical drilling speed,without fully considering the sequential nature of drilling operations and the correlation of me-chanical drilling speed in time series.A mechanical drilling speed prediction model based on the Attention-LSTM architecture with tem-poral 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 improve-ment in prediction accuracy compared to traditional deep neural networks.The added"Attention"mechanism further enhances the in-terpretability,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,demonstra-ting the advantages of this method in accuracy,reliability,and interpretability.关键词
机械钻速/预测模型/时序性/Attention-LSTMKey words
mechanical drilling speed/prediction model/temporality/Attention-LSTM分类
能源科技引用本文复制引用
王彬,徐英卓,刘烨,李燕..基于Attention-LSTM时序模型的机械钻速预测方法[J].西安石油大学学报（自然科学版）,2024,39(5):85-95,11.基金项目
国家自然科学基金项目(52004214) （52004214）
陕西省自然科学基金项目(2021JM-400) （2021JM-400）
