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页岩凝析气藏相平衡的快速准确计算方法

章涛 白桦 孙树瑜

章涛, 白桦, 孙树瑜. 页岩凝析气藏相平衡的快速准确计算方法. 力学学报, 2021, 53(8): 1-12 doi: 10.6052/0459-1879-21-229
引用本文: 章涛, 白桦, 孙树瑜. 页岩凝析气藏相平衡的快速准确计算方法. 力学学报, 2021, 53(8): 1-12 doi: 10.6052/0459-1879-21-229
Zhang Tao, Bai Hua, Sun Shuyu. Fast and accurate phase equilibrium calculations for condensate shale gas reservoirs. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8): 1-12 doi: 10.6052/0459-1879-21-229
Citation: Zhang Tao, Bai Hua, Sun Shuyu. Fast and accurate phase equilibrium calculations for condensate shale gas reservoirs. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8): 1-12 doi: 10.6052/0459-1879-21-229

页岩凝析气藏相平衡的快速准确计算方法

doi: 10.6052/0459-1879-21-229
基金项目: 国家自然科学基金资助项目(51874262, 51936001)
详细信息
    作者简介:

    孙树瑜, 教授, 主要研究方向: 多尺度油藏数值模拟和油藏数字孪生体. E-mail: sfsun@yahoo.com

  • 中图分类号: TE32

FAST AND ACCURATE PHASE EQUILIBRIUM CALCULATIONS FOR CONDENSATE SHALE GAS RESERVOIRS

  • 摘要: 对页岩油气藏中复杂流体的相平衡计算需要建立考虑毛细作用效应的先进的数值模型, 并设计出快速可靠的算法以应对实际工况中储层流体包含多达数十种组分的复杂情况. 本文将基于适合页岩油气藏常见组分的真实流体状态方程, 即Peng−Robinson状态方程构建具有热力学一致性的VT型孔观相平衡计算体系. 通过引入描述毛细压力做功的数学模型实现对页岩流体热力学性质更准确的刻画. 结合扩散界面模型建立动力学演化格式, 采用成熟的凸分裂方法求解摩尔数和体积分数的演变, 从而描述相平衡的动态过程. 在此基础上, 本文开发了一套具有自适应性的深度学习算法, 设计了独特的双网络结构以实现对不同流体中不同组分的广泛适用性. 该神经网络的输入和输出参数均在热力学分析的基础上选取关键的热力学性质参数, 并进行了全面的超参调试以确定最合适的网络架构和最后形成的预测模型的基本结构, 且通过多种深度学习技术解决了过拟合问题, 在显著加速了传统的基于迭代方法的闪蒸计算的同时保证了相平衡状态预测的准确性, 得到了较好的预测效果. 相分离判定自动整合在预测结果中, 且从最终预测结果可以显著地捕捉到毛细作用的影响. 这一套快速、准确、可靠地基于深度学习算法的页岩油气孔观相平衡计算体系可以为后续的多相流动模拟提供具有物理意义的相分布初场, 确定系统内各个阶段的相数, 并可以作为构建具有物理守恒性的多相数值模型的热力学基础.

     

  • 图  1  页岩油气相平衡计算体系

    Figure  1.  Phase equilibrium calculation scheme for shale gas reservoirs

    图  2  用于相平衡预测的深层神经网络架构

    Figure  2.  Deep neural network for phase equilibrium estimates

    图  3  每隐藏层节点数调优

    Figure  3.  Tuning on the number of nodes in each hidden layer

    图  4  隐藏层数调优

    Figure  4.  Tuning on the number of hidden layers

    图  5  激活函数调优

    Figure  5.  Tuning on the activation functions

    图  6  Bakken储层流体在60 mol/m3摩尔浓度时平衡态相数随温度的改变

    Figure  6.  Number of phases existing at equilibrium for Bakken reservoir fluids under constant overall mole concentration as 60 mol/m3

    图  7  Bakken储层流体在60 mol/m3摩尔浓度时甲烷组分在气相的摩尔分数在平衡态随温度的改变

    Figure  7.  Mole fraction of C1 component in the vapor phase at equilibrium for Bakken reservoir fluids changing with temperature and under constant overall mole concentration as 60 mol/m3

    图  8  14组分Eagle Ford储层流体在343 mol/m3摩尔浓度时甲烷和庚烷组分在气相的摩尔分数在平衡态随温度的改变

    Figure  8.  Mole fraction of C1, C7 components in the vapor phase at equilibrium for 14-component Eagle Ford reservoir fluids changing with temperature and under constant overall mole concentration 343 mol/m3

    表  1  Bakken储藏流体性质数据

    Table  1.   Fluid properties of Bakken reservoir

    Component${ {\textit{z}} }_{,i}$${ {T} }_{{\rm{c}},i}$/K${ {P} }_{{\rm{c}},i}$/MPa$ {\omega }_{i} $
    C10.2506190.6064.60000.008
    C2 ~ C40.2200363.304.31000.143
    C5 ~ C70.2000511.563.42100.247
    C8 ~ C90.1300579.343.13200.286
    C10+0.1994788.742.18700.687
    下载: 导出CSV

    表  2  深度学习算法的表现

    Table  2.   Performance of deep learning algorithm

    Fluidtflash/stdl/s$ \mathrm{\varepsilon } $
    with capillarity22147.80.086
    without capillarity20157.50.091
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-27
  • 录用日期:  2021-06-15
  • 网络出版日期:  2021-06-16

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