Prediction of Fluid Flow in Porous Media by Sparse Observations and Physics-Informed PointNet

Ali Kashefi, Tapan Mukerji

Published 2022-08-29Version 1

We predict steady-state Stokes flow of fluids within porous media at pore scales using sparse point observations and a novel class of physics-informed neural networks, called "physics-informed PointNet" (PIPN). Taking the advantages of PIPN into account, three new features become available compared to physics-informed convolutional neural networks for porous medium applications. First, the input of PIPN is exclusively the pore spaces of porous media (rather than both the pore and grain spaces). This feature significantly diminishes computational costs. Second, PIPN represents the boundary of pore spaces smoothly and realistically (rather than pixel-wise representations). Third, spatial resolution can vary over the physical domain (rather than equally spaced resolutions). This feature enables users to reach an optimal resolution with a minimum computational cost. Performance of our framework is evaluated by the study of influence of noisy sensor data, pressure observations, and spatial correlation length.