Employing Deep Learning Neural Networks for Characterizing Dual-Porosity Reservoirs Based on Pressure Transient Tests

Abstract

The deep learning model constituting two neural network models (i.e., densely connected and long short-term memory) has been applied for automatic characterization of dual-porosity reservoirs with infinite, constant pressure, and no-flow external boundaries. A total of 16 different prediction paradigms have been constructed (one classifier to identify the reservoir models and 15 regressors for predicting the dual-porosity reservoir characteristics). Indeed, wellbore storage coefficient, CDe2S, skin factor, interporosity flow coefficient, and storativity ratio have been estimated. The training pressure signals have been simulated using the analytical solution of the governing equations with varying noise percentages. The pressure drop and derivation of the noisy synthetic signals serve as the input signals to the intelligent scenario. The hyperparameters of the intelligent model have been carefully adjusted to improve its prediction performance. The trained classification model attained 99.48% and 99.32% accuracy over the training and testing datasets. The separately trained 15 regressors converged well to estimate the reservoir parameters. The model performance has been demonstrated with three uniquely simulated and real-field cases. The results indicate that the compiled prediction model can accurately identify the reservoir model and estimate the corresponding characteristics.