Backpropagation neural networks for solving gas flow equations in porous media
Keywords:
nonlinear solver, neural networks, backpropagation, reservoir simulation, gas flowAbstract
The mathematical equations involved in oil and gas reservoir simulations are typically extensive and nonlinear. This study presents a new approach based on backpropagation neural networks for solving the system of nonlinear equations that arises from hydrocarbon reservoir modeling. This proposed solver employs feed-forward neural networks and consists of an input layer, two hidden layers, and an output layer. Later, we use this proposed solver to solve one-dimensional gas flow problems in a porous medium and verify its accuracy with a solution from the classic Newton method. The pressure solution resulting from the neural network-based solver resembles the Newton method solution. The simulation results also show that the learning rate parameters in networks influence the convergence speed and accelerate weight updates. However, excessively high learning rates can cause weights to overshoot.
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