基于深度学习的代理流建模和CNN-PCA地质参数化的复杂三维系统历史匹配

Meng Tang, Yimin Liu, L. Durlofsky
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引用次数: 4

摘要

使用基于深度学习的程序进行地质参数化和快速代理流建模,可以应用严格的历史匹配算法,这在以前被认为是不切实际的。在本研究中,我们将这些方法-特别是地质参数化,需要主成分分析与卷积神经网络(CNN-PCA)相结合,以及使用循环残差u - net程序的流量代理-纳入三种不同的历史匹配程序。考虑的历史匹配算法有拒绝抽样(RS)、随机最大似然网格自适应直接搜索优化(MADS-RML)和多数据同化集成平滑(ES-MDA)。RS是一种严格的采样器,用于提供参考结果(尽管在有大量观察数据的情况下它可能变得难以处理)。对定义在包含128,000个单元格的网格上的通道化几何模型执行历史匹配。地质实现的CNN-PCA表示涉及400个参数,这些参数是通过历史匹配确定的变量。所有流量评估(训练后)都使用循环残差u - net代理模型进行。本文考虑了涉及不同数量历史数据的两种情况。我们发现MADS-RML和ES-MDA提供的历史匹配结果与RS基本一致,但MADS-RML更准确,ES-MDA在某些数量上可能显示显着误差。然而,ES-MDA比MADS-RML需要更少的函数评估,因此需要在计算需求和准确性之间进行权衡。这里开发的框架可用于评估和调整一系列历史匹配过程,而不仅仅是本文所考虑的那些过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
History Matching Complex 3D Systems Using Deep-Learning-Based Surrogate Flow Modeling and CNN-PCA Geological Parameterization
The use of deep-learning-based procedures for geological parameterization and fast surrogate flow modeling may enable the application of rigorous history matching algorithms that were previously considered impractical. In this study we incorporate such methods – specifically a geological parameterization that entails principal component analysis combined with a convolutional neural network (CNN-PCA) and a flow surrogate that uses a recurrent residual-U-Net procedure – into three different history matching procedures. The history matching algorithms considered are rejection sampling (RS), randomized maximum likelihood with mesh adaptive direct search optimization (MADS-RML), and ensemble smoother with multiple data assimilation (ES-MDA). RS is a rigorous sampler used here to provide reference results (though it can become intractable in cases with large amounts of observed data). History matching is performed for a channelized geomodel defined on a grid containing 128,000 cells. The CNN-PCA representation of geological realizations involves 400 parameters, and these are the variables determined through history matching. All flow evaluations (after training) are performed using the recurrent residual-U-Net surrogate model. Two cases, involving different amounts of historical data, are considered. We show that both MADS-RML and ES-MDA provide history matching results in general agreement with those from RS. MADS-RML is more accurate, however, and ES-MDA can display significant error in some quantities. ES-MDA requires many fewer function evaluations than MADS-RML, however, so there is a tradeoff between computational demand and accuracy. The framework developed here could be used to evaluate and tune a range of history matching procedures beyond those considered in this work.
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