Petroleum Science >2022,??lssue 2:??- DOI: https://doi.org/10.1016/j.petsci.2021.10.007
Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex Open?Access
文章信息
作者：Kai Zhang, Hai-Qun Yu, Xiao-Peng Ma, Jin-Ding Zhang, Jian Wang, Chuan-Jin Yao, Yong-Fei Yang, Hai Sun, Jun Yao, Jian Wang,
作者單位：
投稿時(shí)間：
引用方式：Kai Zhang, Hai-Qun Yu, Xiao-Peng Ma, Jin-Ding Zhang, Jian Wang, Chuan-Jin Yao, Yong-Fei Yang, Hai Sun, Jun Yao, Jian Wang, Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies, Petroleum Science, Volume 19, Issue 2, 2022, Pages 707-719,
文章摘要
Abstract
For reservoirs with complex non-Gaussian geological characteristics, such as carbonate reservoirs or reservoirs with sedimentary facies distribution, it is difficult to implement history matching directly, especially for the ensemble-based data assimilation methods. In this paper, we propose a multi-source information fused generative adversarial network (MSIGAN) model, which is used for parameterization of the complex geologies. In MSIGAN, various information such as facies distribution, microseismic, and inter-well connectivity, can be integrated to learn the geological features. And two major generative models in deep learning, variational autoencoder (VAE) and generative adversarial network (GAN) are combined in our model. Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation (ESMDA) method to conduct history matching. We tested the proposed method on two reservoir models with fluvial facies. The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features, which can promote the accuracy of history matching.
For reservoirs with complex non-Gaussian geological characteristics, such as carbonate reservoirs or reservoirs with sedimentary facies distribution, it is difficult to implement history matching directly, especially for the ensemble-based data assimilation methods. In this paper, we propose a multi-source information fused generative adversarial network (MSIGAN) model, which is used for parameterization of the complex geologies. In MSIGAN, various information such as facies distribution, microseismic, and inter-well connectivity, can be integrated to learn the geological features. And two major generative models in deep learning, variational autoencoder (VAE) and generative adversarial network (GAN) are combined in our model. Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation (ESMDA) method to conduct history matching. We tested the proposed method on two reservoir models with fluvial facies. The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features, which can promote the accuracy of history matching.
關(guān)鍵詞
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Multi-source information; Automatic history matching; Deep learning; Data assimilation; Generative model