multiphase flow

reservoir characterization

history matching

reservoir simulation

networks pinns

Fuel

• We develop an efficient physics-constrained deep learning model to solve the problem of multiphase flow in 3-Dimenionsal heterogeneous porous media; • We implement numerical techniques including continuity-based smoother and spatial domain decomposition to improve the prediction accuracy of the state variables, especially pressure; • We penalize the training loss in the pressure and saturation plume regions, to steer the training process such that the model can accurately capture flow in these regions; • We validate the whole workflow through the example of C O 2 injection in heterogeneous geological formations. Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological C O 2 sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples...

A physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media