A robust deep structured prediction model for petroleum reservoir characterization using pressure transient test data

A robust deep learning model consisting of long short-term memory and fully connected neural networks has been proposed to automatically interpret homogeneous petroleum reservoirs having infinite, no flow, and constant pressure outer boundary conditions. The pressure change data recorded during the well test operation along with its derivative is input into the model to perform the classification for identifying the reservoir model and, further, regression to estimate output parameter. Gaussian noise was added to analytical models while generating the synthetic training data. The hyperparameters were regulated to perform model optimization, resulting in a batch size of 64, Adam optimization algorithm, learning rate of 0.01, and 80:10:10 data split ratio as the best choices of hyperparameters. The performance accuracy also increased with an increase in the number of samples during training. Suitable classification and regression metrics have been used to evaluate the performance of the models. The paper also demonstrates the prediction performance of the optimized model using simulated and actual oil well pressure drawdown test cases. The proposed model achieved minimum and maximum relative errors of 0.0019 and 0.0308, respectively, in estimating output for the simulated test cases and relative error of 0.0319 for the real test case.