A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

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In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3D heterogeneous porous media.

• The model fully leverages the spatial topology predictive capability of convolutional neural networks, 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).