Training image (TI) has a great influence on reservoir modeling as a spatial correlation in the multipoint geostatistics. Unlike the variogram of the two-point geostatistics that is mathematically defined, there is a high degree of geological uncertainty to determine a proper TI. The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. The training data are set to cover various production trends to make the machine learning models more reliable. Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. We divided them into training, validation, and test sets of 576, 144, and 80, respectively. The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. The SVM and CNN models reasonably reduced the uncertainty in modeling the facies distribution based on the reliable probability for each TI. Even though the ANN and CNN had roughly the same number of parameters, the CNN outperformed the ANN in terms of both validation and test sets. The CNN successfully classified the reference model’s TI with about 95% probability. This is because the CNN can grasp the overall trend of production history. The probabilities of TI from the SVM and CNN were applied to regenerate more reliable reservoir models using the concept of TI rejection and reduced the uncertainty in the geological scenario successfully.

中文翻译：

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使用基于机器学习的生产数据分类模型管理地质情景中的不确定性

训练图像 (TI) 作为多点地质统计学中的空间相关性对储层建模有很大影响。与数学定义的两点地质统计学的变异函数不同，确定合适的 TI 存在高度的地质不确定性。本研究的目标是开发一个分类模型，通过使用机器学习方法确定合理的 TI 中的适当地质场景：(a) 支持向量机 (SVM)，(b) 人工神经网络 (ANN)，和 (c)卷积神经网络 (CNN)。使用模拟生产数据训练分类模型后，将观察到的生产响应放入训练模型时，可以选择最可能的TI。据我们所知，这项研究 是 CNN 的第一个应用，其中生产历史数据组成矩阵形式用作输入图像。训练数据设置为涵盖各种生产趋势，使机器学习模型更加可靠。因此，考虑到地质不确定性，4 个 TI 共产生了 800 个通道化储层，它们具有不同的通道方向。我们将它们分为训练集、验证集和测试集，分别为 576、144 和 80。输入层包含 800 个生产数据，即 8 口生产井在 50 个时间步长内的采油率和含水率，输出层包含每个 TI 的概率向量。SVM 和 CNN 模型基于每个 TI 的可靠概率，合理降低了对相分布建模的不确定性。尽管 ANN 和 CNN 的参数数量大致相同，但 CNN 在验证集和测试集方面都优于 ANN。CNN 以大约 95% 的概率成功地对参考模型的 TI 进行了分类。这是因为CNN可以掌握生产历史的整体趋势。来自 SVM 和 CNN 的 TI 概率被应用于使用 TI 拒绝的概念重新生成更可靠的储层模型，并成功降低了地质场景中的不确定性。