  Fast prediction of permeability directly from images enabled by image
recognition neural networks is a novel pore-scale modeling method that has a
great potential. This article presents a framework that includes (1) generation
of porous media samples, (2) computation of permeability via fluid dynamics
simulations, (3) training of convolutional neural networks (CNN) with simulated
data, and (4) validations against simulations. Comparison of machine learning
results and the ground truths suggests excellent predictive performance across
a wide range of porosities and pore geometries, especially for those with
dilated pores. Owning to such heterogeneity, the permeability cannot be
estimated using the conventional Kozeny-Carman approach. Computational time was
reduced by several orders of magnitude compared to fluid dynamic simulations.
We found that, by including physical parameters that are known to affect
permeability into the neural network, the physics-informed CNN generated better
results than regular CNN, however improvements vary with implemented
heterogeneity.
