Prediction of diffusional conductance in extracted pore network models using convolutional neural networks

Pore network modeling (PNM) based on networks extracted from tomograms is a well-established tool for simulating pore-scale transport behavior in porous media. A key element of this approach is the accurate determination of pore-to-pore conductance values, which is a complex task that greatly affects the accuracy of flow and diffusive mass transport studies. Classic methods of conductance estimation based on analytical solu-tions and shape factors only apply to simple pore geometries, whereas real porous media contain irregular-shaped pores. Although direct numerical simulations (DNS) can accurately estimate conductance considering pores' real morphology, it has a high computational cost that becomes infeasible for large tomograms. The present work remedies this problem using a deep learning (DL) approach, with a specific focus on diffusional transport which has received less attention than hydraulic conductance. A convolutional neural network (CNN) model was trained to estimate diffusive conductance of PNM elements from volumetric images of porous media. The developed framework estimates the diffusive conductance by analyzing individual pore-to-pore 3D images isolated from the tomogram to fully capture the topology and shapes. A key outcome of the present work is that only images of the pore regions are used as input data, avoiding excessive preprocessing time for data prepa-ration. The results of the diffusive conductance prediction show good agreement with the test data obtained by DNS method, with 0.94 R-2 prediction accuracy and a speedup of 500x in prediction runtime.

Automated summary

This study develops a deep learning framework to accurately predict the diffusive conductance in porous media by analyzing 3D images of pore regions extracted from tomograms. The results demonstrate high accuracy and significantly improved prediction speed.