Physics Informed Neural Networks Based on Sequential Training for CO2 Utilization and Storage in Subsurface Reservoir

The energy transition is unavoidable because energy production accounts for roughly two-thirds of current global emissions. To achieve this goal, active carbon emission reduction is required. carbon dioxide capture, utilization, and storage (CCUS) is a promising technology to mitigate global warming. In order to operate CCUS intelligently, there must be a robust simulation technology that captures physics and the expected scenario. Machine learning (ML) techniques have recently been applied to a wide range of nonlinear computational problems. Recently, Physics informed neural network (PINN) has been proposed for solving partial differential equations. Unlike typical ML algorithms that require a large dataset for training, PINN can train the network with unlabelled data. The applicability of this method has been explored for flow and transport of multiphase in porous media. However, for strongly nonlinear hyperbolic transport equation, the solution degrades significantly. In this work, we propose a sequential training PINN to simulate two-phase transport in porous media. The main concept is to retrain neural network to solve the PDE over successive time segments rather than train for the entire time domain at once. We observe that, sequential training can capture the solution more accurately with respect to standard training method.