Application of Recurrent Networks to Classification of Oil Reservoir Models in Well-testing Analysis

Abstract

The main objective of this study is utilization of recurrent neural networks to categorize pressure derivative plots of well-testing data into various reservoir models. The training and test data have been generated through an analytical solution of commonly used reservoir models. The accuracy of the designed recurrent neural networks has been examined by the simulation test data and actual field data. The accuracy of the developed recurrent neural networks has been compared to a multilayer perceptron neural network. The results indicate that the recurrent neural networks can identify the correct reservoir models from test data with an accuracy of 98.39%, while multilayer perceptron neural networks represent an accuracy of 95.83%.