Graph Convolutional Networks for Simulating Multi-phase Flow and Transport in Porous Media

arXiv:2307.04449v2 Announce Type: replace-cross
Abstract: Numerical simulation of multi-phase fluid dynamics in porous media is critical for many energy and environmental applications in Earth's subsurface. Data-driven surrogate modeling provides computationally inexpensive alternatives to high-fidelity numerical simulators. While the commonly used convolutional neural networks (CNNs) are powerful in approximating partial differential equation solutions, it remains challenging for CNNs to handle irregular and unstructured simulation meshes. However, simulation models for Earth's subsurface often involve unstructured meshes with complex mesh geometries, which limits …

abstract applications arxiv cnns convolutional neural networks cs.lg data data-driven dynamics earth energy environmental fidelity flow fluid dynamics graph media modeling networks neural networks numerical physics.comp-ph simulation transport type