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基于物理经验模型约束的机器学习方法在页岩油产量预测中的应用

周济民 张海晨 王沫然

周济民, 张海晨, 王沫然. 基于物理经验模型约束的机器学习方法在页岩油产量预测中的应用[J]. 应用数学和力学, 2021, 42(9): 881-890. doi: 10.21656/1000-0887.420015
引用本文: 周济民, 张海晨, 王沫然. 基于物理经验模型约束的机器学习方法在页岩油产量预测中的应用[J]. 应用数学和力学, 2021, 42(9): 881-890. doi: 10.21656/1000-0887.420015
ZHOU Jimin, ZHANG Haichen, WANG Moran. Machine Learning With Physical Empirical Model Constraints for Prediction of Shale Oil Production[J]. Applied Mathematics and Mechanics, 2021, 42(9): 881-890. doi: 10.21656/1000-0887.420015
Citation: ZHOU Jimin, ZHANG Haichen, WANG Moran. Machine Learning With Physical Empirical Model Constraints for Prediction of Shale Oil Production[J]. Applied Mathematics and Mechanics, 2021, 42(9): 881-890. doi: 10.21656/1000-0887.420015

基于物理经验模型约束的机器学习方法在页岩油产量预测中的应用

doi: 10.21656/1000-0887.420015
基金项目: 

国家重点研发项目(2019YFA0708704)

详细信息
    作者简介:

    周济民(2000—), 男(E-mail: zhoujm18@mails.tsinghua.edu.cn);王沫然(1977—), 男, 教授, 博士, 博士生导师(通讯作者. E-mail: mrwang@tsinghua.edu.cn).

    通讯作者:

    王沫然(1977—), 男, 教授, 博士, 博士生导师(通讯作者. E-mail: mrwang@tsinghua.edu.cn).

  • 中图分类号: O368|O29

Machine Learning With Physical Empirical Model Constraints for Prediction of Shale Oil Production

  • 摘要: 页岩油气产量预测是确定其开发经济性的重要手段,目前的产量预测研究很少能在物理模型与数据挖掘方法之间达到统一.针对页岩油气的产量分析,本研究深入结合误差反向传递(BP)神经网络和长短期记忆(LSTM)神经网络的数学方法优势,综合考虑工程经验模型的约束,改善了模型预测精度,经过实例数据训练后可较好地预测油田产量,并研究了页岩储层深度、总有机碳含量(TOC)、脆性度等油田参数对产量预测的影响规律.这项工作可以为页岩油气规模化开发提供可靠的产量预测和经济评价.
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出版历程
  • 收稿日期:  2021-01-14
  • 修回日期:  2021-04-06
  • 网络出版日期:  2021-09-29

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