Abstract

An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network (CNN) is proposed, and its effectiveness and accuracy are verified by actual field data. In this paper, based on the data transformed by logarithm function and the loss function of mean square error (MSE), the optimal CNN is obtained by reducing the loss function to optimize the network with “dropout” method to avoid over fitting. The trained optimal network can be directly used to interpret the buildup or drawdown pressure data of the well in the radial composite reservoir, that is, the log-log plot of the given measured pressure variation and its derivative data are input into the network, the outputs are corresponding reservoir parameters (mobility ratio, storativity ratio, dimensionless composite radius, and dimensionless group characterizing well storage and skin effects), which realizes the automatic initial fitting of well test interpretation parameters. The method is verified with field measured data of Daqing Oilfield. The research shows that the method has high interpretation accuracy, and it is superior to the analytical method and the least square method.

中文翻译：

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基于卷积神经网络的径向复合油藏自动试井解释

摘要

提出了一种基于卷积神经网络的径向复合油藏自动试井解释方法，并通过现场实测数据验证了其有效性和准确性。本文基于对数函数和均方误差（MSE）的损失函数转换的数据，通过减少损失函数来优化网络的最佳CNN，该损失函数采用“丢包”方法进行优化以避免过度拟合。训练后的最优网络可以直接用于解释径向复合储层中井的增建或降落压力数据，即将给定的测得压力变化的对数-对数图及其导数输入到网络中，输出是相应的储层参数（迁移率，储能比，无因次复合半径，以及无因次分组来表征井的储藏和集肤效应），从而实现对试井解释参数的自动初始拟合。大庆油田现场实测数据验证了该方法的有效性。研究表明，该方法具有较高的解释精度，优于解析法和最小二乘法。