CO2 sequestration which is one of the important means to avoid global warming today provides an economically viable technical means to reduce greenhouse gas emissions. To maximize the sequestration efficiency while evaluating the effect of formation uncertainty, a numerical simulator of multiphase flow is required to simulate high-dimensional nonlinear multiphase flow within a non-homogeneous porous medium. Due to the inherent inhomogeneity of the formation of porous media and the nonlinear coupling of multiple complex physical processes, a significant amount of repetitive numerical simulation processes impose considerable computational costs and require prolonged computational time to obtain simulation results. In this paper, we propose an efficient and fast flow surrogate modeling process for deep learning, proposing that the extended hyperparameter optimization process will incorporate the neural network architecture and the loss function as relevant parameters into the optimization process. Subsequently, we conducted experiments based on the workflow proposed in this study for the case of CO2 storage in a homogeneous deep saltwater layer and achieved accurate predictions at 120-time steps with mean MSE of 5E-5 and 2E-5 for gas saturation and pressure, and MSSIMs of 0.9989 and 0.9998, respectively, under different production parameters and well placement settings.
Keywords Numerical simulation, Surrogate model, Deep-learning, Geological sequestration