Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media

Understand

We present efficient deep learning techniques for approximating flow and transport equations for both single phase and two-phase flow problems.

• The proposed methods take advantages of the sparsity structures in the underlying discrete systems and can be served as efficient alternatives to the system solvers at the full order.
• In particular, for the flow problem, we design a network with convolutional and locally connected layers to perform model reductions.
• Moreover, we employ a custom loss function to impose local mass conservation constraints.