Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS. (arXiv:2109.01818v2 [cs.LG] UPDATED)

In recent years, convolutional neural networks (CNNs) have experienced an
increasing interest in their ability to perform a fast approximation of
effective hydrodynamic parameters in porous media research and applications.
This paper presents a novel methodology for permeability prediction from
micro-CT scans of geological rock samples. The training data set for CNNs
dedicated to permeability prediction consists of permeability labels that are
typically generated by classical lattice Boltzmann methods (LBM) that simulate
the flow through the pore space of the …

3d arxiv cnns dns images physics