Prediction and parameter optimization of depressurization productivity of natural gas hydrate reservoirs based on neural network

In order to develop a fast and efficient numerical simulation technique for productivity prediction of natural gas hydrate reservoirs, a neural network model was established by learning the results of conventional reservoir simulations, in which the learning samples of the new model were established based on the geological data of actual hydrate reservoirs. The model was applied for a two-year period of production prediction of the hydrate reservoirs in the Shenhu and the Nankai Trough regions in terms of depressurization. The simulation results indicate that the prediction accuracy of the neural network model exceeds 97% in comparison with the conventional reservoir simulators. The predicted average gas productions rate for the Shenhu hydrate reservoir is 2 839 m3 / d, with an optimal production pressure of 3 MPa. For the hydrate reservoir in the Nankai Trough,the predicted two-year average gas productions rate is 21 523 m3 / d, and the optimal production pressure is recommended as 4 MPa. The results of productivity prediction for various hydrate reservoirs indicate that production pressure of 3 MPa is suitable for nearly 69% of the hydrate reservoirs studied. However, there commended production pressure can be better of 5 MPa for reservoirs with hydrate saturation greater than 65%, the absolute permeability higher than 0. 1 μm2 and the initial reservoir pressure higher than 20 MPa. © 2023 University of Petroleum, China. All rights reserved.