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首页> 外文期刊>Fuel >Assisted history matching in shale gas well using multiple-proxy-based Markov chain Monte Carlo algorithm: The comparison of K-nearest neighbors and neural networks as proxy model
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Assisted history matching in shale gas well using multiple-proxy-based Markov chain Monte Carlo algorithm: The comparison of K-nearest neighbors and neural networks as proxy model

机译：使用基于多功能的马尔可夫链蒙特卡罗算法的页岩气井辅助历史匹配：K-Collest邻居和神经网络作为代理模型的比较

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摘要

We performed assisted history matching (AHM) in a real shale gas well using uncertain parameters such as fracture geometry, fracture conductivity, matrix permeability, matrix and fracture water saturation, and relative permeability curves. We also investigated the performance of two proxy models including K-nearest neighbors (KNN) and neural networks (NN) to be used in multiple-proxy-based Markov chain Monte Carlo (MCMC) algorithm. We emphasized the performance of both proxy models by comparing the number of history matching solution found and elapsed time. While, KNN required less elapsed time by half than NN, we found that NN performed better in terms of accuracy and predictability than KNN. In other words, NN required a smaller number of simulations by half than KNN in order to obtain the same number of history matching solutions. Therefore, it depends on what is more important to each problem either number of simulations or elapsed time. For history matching result, both proxy models in the multiple-proxy-based MCMC algorithm have similar results of posterior distribution of uncertain parameters. This confirms the robustness of the proposed history matching algorithm. The benefits of this study are that we can characterize fracture geometry and reservoir properties in a probabilistic manner. These multiple realizations can be further used for a probabilistic production forecast, future fracturing design, and well spacing optimization and planning. This AHM workflow can be applied to any hydraulic-fractured wells with historical production data.
机译：我们使用不确定的参数在真正的页岩气中进行了辅助历史匹配（AHM），例如断裂几何形状，断裂导电性，基质渗透性，基质和裂缝水饱和度，以及相对渗透曲线。我们还调查了两个代理模型的性能，包括k最近邻居（knn）和神经网络（Nn），以用于基于多个代理的马尔可夫链蒙特卡罗（MCMC）算法。我们通过比较找到和经过时间的历史匹配解决方案的数量来强调两种代理模型的性能。虽然，KNN所需的时间较少时间超过NN，我们发现NN在比KNN的准确性和可预测性方面更好地执行。换句话说，NN需要较少数量的仿真，以获得相同数量的历史匹配解决方案。因此，这取决于每个问题的仿真数或经过时间更重要。对于历史匹配结果，基于多代理的MCMC算法中的两个代理模型具有相似的不确定参数的后部分布结果。这证实了所提出的历史匹配算法的鲁棒性。本研究的益处是我们可以以概率的方式表征断裂几何和储层性质。这些多种实现可以进一步用于概率的生产预测，未来压裂设计以及井间距优化和规划。该AHM工作流程可以应用于具有历史生产数据的任何液压骨折井。

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  • 来源
    《Fuel》 |2020年第15期|116563.1-116563.15|共15页
  • 作者

    Tripoppoom Sutthaporn; Ma Xinhua; Yong Rui; Wu Jianfa; Yu Wei; Sepehrnoori Kamy; Miao Jijun; Li Ning;

  • 作者单位

    Univ Texas Austin Hildebrand Dept Petr & Geosyst Engn Austin TX 78712 USA;

    Petrochina Southwest Oil&Gas Field Co Chengdu 610017 Sichuan Peoples R China;

    Petrochina Southwest Oil&Gas Field Co Chengdu 610017 Sichuan Peoples R China;

    Petrochina Southwest Oil&Gas Field Co Chengdu 610017 Sichuan Peoples R China;

    Univ Texas Austin Hildebrand Dept Petr & Geosyst Engn Austin TX 78712 USA;

    Univ Texas Austin Hildebrand Dept Petr & Geosyst Engn Austin TX 78712 USA;

    Univ Texas Austin Hildebrand Dept Petr & Geosyst Engn Austin TX 78712 USA|Petrochina Southwest Oil&Gas Field Co Chengdu 610017 Sichuan Peoples R China;

    Sim Tech LLC Houston TX USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Assisted history matching; EDFM; Markov chain Monte Carlo; K-nearest neighbors; Neural networks;

    机译：辅助历史匹配;EDFM;马尔可夫链蒙特卡洛;K-Etcleend邻居;神经网络;

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