An automatic history matching method based on ensemble and neural architecture search

被引：0

作者：

Zhang L. ^{[1
]}

Chen X. ^{[1
]}

Li G. ^{[2
]}

Ma X. ^{[1
]}

Zhang K. ^{[1
]}

Gu J. ^{[1
]}

Yao J. ^{[1
]}

Wang J. ^{[3
]}

Sun H. ^{[1
]}

机构：

[1] School of Petroleum Engineering in China University of Petroleum(East China), Qingdao

[2] PetroChina Exploration and Production Company, Beijing

[3] College of Science in China University of Petroleum(East China), Qingdao

来源：

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2022年 / 46卷 / 02期

关键词：

Automatic history matching; Complex geological features; Deep auto-encoder; Deep learning; Ensemble smoother; Neural architecture search;

D O I：

10.3969/j.issn.1673-5005.2022.02.013

中图分类号：

学科分类号：

摘要：

Due to the limitations of manual selection via human experience, it is difficult to obtain the optimal network parameters that determine the accuracy of model reconstruction, which is currently one of the difficulties when using deep learning methods to reduce the dimensionality of reservoir geological models in automatic history matching. In response to this, the automatic search of the best network architecture was realized by combining the deep auto-encoder and the particle swarm optimization algorithm, and an automatic reservoir history matching method was constructed, based on aggregate data assimilation and automatic search of the neural network architecture. A two-dimensional permeability distribution model of a fluvial reservoir and a SPE-10 single-layer reservoir numerical model were used to verify the proposed method, in comparison with a single automatic history matching method. The results show that the automatic history matching method can automatically searches the optimal neural network framework after optimization, which can extract the geological characteristics from the reservoir numerical model more accurately than the single automatic history matching method. © 2022, Editorial Office of Journal of China University of Petroleum(Edition of Natural Science). All right reserved.

引用

页码：127 / 136

页数：9

共 32 条

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