中国石油大学(华东)青岛软件学院计算机科学与技术学院
纸质出版:2025
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张晓东, 李敏. 基于WPD-FEEMD和ARIMA-LSTM的油井产量预测方法[J]. 传感器与微系统, 2025,(6):161-164+168.
张晓东, 李敏. 基于WPD-FEEMD和ARIMA-LSTM的油井产量预测方法[J]. 传感器与微系统, 2025,(6):161-164+168. DOI: 10.13873/J.1000-9787(2025)06-0161-04.
针对油井生产过程中间歇开关井等人工操作导致产量序列非线性波动、非线性趋势混叠等问题,提出了一种混合二次分解算法和差分自回归综合移动平均—长短期记忆网络(ARIMA-LSTM)的单井产量预测方法。该方法首先采用小波包分解(WPD)将原始产量序列分解为低频分量和高频分量;然后采用快速集合经验模态分解(FEEMD)分解高频分量,进一步降低高频分量的非平稳性,同时去除模式混叠;针对各子序列,分别构建基于ARIMA-LSTM的时序预测模型,该模型使用ARIMA过滤序列中的线性趋势,并将残差传递给Bi-LSTM提取非线性趋势;最后融合各子序列预测结果,得到油井产量预测值。算例研究结果表明,相较于支持向量回归(SVR)、LSTM等模型,所提方法具有更高的预测精度。
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School of Earth Science and Engineering, Xi'an Shiyou University, Xi'an 710065, China,State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology), Chengdu, Sichuan 610059, China,Shanxi Key Laboratory of Petroleum Accumulation Geology, School of Earth Science and Engineering, Xi'an Shiyou University, Shanxi Xi'an 710065, China,Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals, Shandong University of Science and Technology, Qingdao, Shandong 266590, China,College of Civil Engineering, Guizhou University, Guiyang 550025, China,Shandong Land & Mining Group Co.,Ltd., Jinan 250000, China.Developmental characteristics, influencing factors and prediction of fractures for a tight gas sandstone in a gentle structural area of the Ordos Basin, China[J].Journal of Natural Gas Science and Engineering,2019.
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