Machine learning method has gradually become an important and effective method to analyze reservoir parameters in reservoir numerical simulation. This paper provides a machine learning method to evaluate the connectivity between injection and production wells controlled by interlayer in reservoir. In this paper, Back Propagation (BP) and Convolutional Neural Networks (CNNs) are used to train the dynamic data with the influence of interlayer control connectivity in the reservoir layer as the training model. The dataset is trained with dynamic production data under different permeability, interlayer dip angle, and injection pressure. The connectivity is calculated by using the deep learning model, and the connectivity factor is defined. The results show that compared with BP, CNN has better performance in connectivity, average absolute relative deviation (AARD) below 10.01% higher. Moreover, CNN prediction results are close to the traditional methods. This paper provides new insights and methods to evaluate the interwell connectivity in conventional or unconventional reservoirs.

中文翻译：

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利用机器学习方法评价油藏地层内层影响下的井间连通性的潜力

机器学习方法已逐渐成为储层数值模拟中分析储层参数的重要有效方法。本文提供了一种机器学习方法来评估油藏夹层控制的注入井和生产井之间的连通性。本文采用反向传播（BP）和卷积神经网络（CNN），以储层层间控制连通性的影响为训练模型，对动态数据进行训练。该数据集使用不同渗透率、层间倾角和注入压力下的动态生产数据进行训练。使用深度学习模型计算连通性，并定义连通性因子。结果表明，与BP相比，CNN在连通性上有更好的表现，平均绝对相对偏差 (AARD) 低于 10.01%。而且，CNN预测结果接近传统方法。本文为评估常规或非常规储层的井间连通性提供了新的见解和方法。