This paper presents convolutional neural network architectures for integration of dynamic flow response data to reduce the uncertainty in geologic scenarios and calibrate subsurface flow models. The workflow consists of two steps, where in the first step the solution search space is reduced by eliminating unlikely geologic scenarios using distinguishing salient flow data trends. The first step serves as a pre-screening to remove unsupported scenarios from the full model calibration process in the second step. For this purpose, a convolutional neural network (CNN) with a cross-entropy loss function is designed to act as a classifier in predicting the likelihood of each scenario based on the observed flow responses. In the second step, the selected geologic scenarios are used in another CNN with an ℓ₂-loss function (as a regression model) to perform model calibration. The regression CNN model (step 2) learns the inverse mapping from the production data space to the low-rank representation of the model realizations within the feasible set. Once the model is trained off-line, a fast feed-forward operation on the observed historical production data (input) is used to reconstruct a calibrated model. The presented approach offers an opportunity to utilize flow data in identifying plausible geologic scenarios, results in an off-line implementation that is conveniently parallellizable, and can generate calibrated models in real time, i.e., upon availability of data and without in-depth technical expertise about model calibration. Several synthetic Gaussian and non-Gaussian examples are used to evaluate the performance of the method.

中文翻译：

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卷积神经网络（CNN）用于不确定地质情况下基于特征的模型校准

本文提出了用于动态流响应数据集成的卷积神经网络体系结构，以减少地质场景中的不确定性并校准地下流模型。工作流程包括两个步骤，其中第一步通过使用明显的流量数据趋势来消除不太可能的地质情况，从而减少了解决方案的搜索空间。第一步是进行预筛选，以从第二步的完整模型校准过程中删除不支持的方案。为此，具有交叉熵损失函数的卷积神经网络（CNN）被设计为基于观测到的流响应预测每种情况的可能性的分类器。第二步，将所选地质方案用于另一个CN _2的CNN-损失函数（作为回归模型）以执行模型校准。回归CNN模型（第2步）学习从生产数据空间到可行集中模型实现的低秩表示的逆映射。离线训练模型后，对观察到的历史生产数据（输入）进行快速前馈操作即可重建校准后的模型。所提出的方法提供了一个机会，可以利用流量数据来识别合理的地质情况，实现可以方便地并行化的离线实施方案，并且可以实时生成校准模型，即在数据可用的情况下并且无需深入的技术专长关于模型校准。使用几个合成的高斯和非高斯示例来评估该方法的性能。