煤矿安全2023，Vol.54Issue(12)：11-17,7.DOI:10.13347/j.cnki.mkaq.2023.12.004
基于注意力机制的CNN-GRU煤层气产能预测方法研究
Prediction of coalbed methane well productivity based on attention mechanism of CNN-GRU
摘要
Abstract
In order to optimize the current prediction method of coalbed methane production and solve the problem of losing key in-formation due to the influence of historical state of coalbed methane production data and too long sequence,a coalbed methane pro-duction prediction model based on attention mechanism of convolutional neural network was proposed.Firstly,the five drainage parameters with high correlation with daily gas production are selected,and the CBM productivity prediction model is constructed and trained based on the field drainage data of the well in Hancheng Block.Finally,the production capacity of the well area in the next 160 days is predicted by the model.The research results show that combined model CNN-GRUA with attention mechanism overcomes the problems that traditional prediction methods cannot deal with,including nonlinearity between data,time sequence and information loss,by extracting high-level features of data and learning the correlation of time series.Compared with BP neural net-work,convolutional neural network(CNN),gated recurrent unit(GRU)and CNN-GRU without attention mechanism model,the CNN-GRUA combined model has higher prediction accuracy,and the mean absolute percentage error is 1.72%.关键词
煤层气产能预测/深度学习/卷积神经网络/门控循环单元/注意力机制Key words
coalbed methane productivity prediction/deep learning/convolutional neural networks/gated recurrent units/attention mechanism分类
矿业与冶金引用本文复制引用
赵海峰,诸立凯,刘长松,张先凡..基于注意力机制的CNN-GRU煤层气产能预测方法研究[J].煤矿安全,2023,54(12):11-17,7.基金项目
国家自然科学基金资助项目(11672333) （11672333）
