MAGNET: Medial Axis Guided Network Extraction Tool

Michael McKague; Hamed Fathiannasab; Mohammad Amin Sadeghi; Jeff Gostick

doi:10.69631/g47x8w91

Authors

Michael McKague University of Waterloo, Department of Chemical Engineering, ON, Canada https://orcid.org/0000-0002-2792-5893
Hamed Fathiannasab University of Waterloo, Department of Chemical Engineering, ON, Canada https://orcid.org/0000-0002-2416-139X
Mohammad Amin Sadeghi University of Waterloo, Department of Chemical Engineering, ON, Canada https://orcid.org/0000-0002-6756-9117
Jeff Gostick University of Waterloo, Department of Chemical Engineering, ON, Canada https://orcid.org/0000-0001-7736-7124

DOI:

https://doi.org/10.69631/g47x8w91

Keywords:

Network extraction, Pore network modelling, Image processing, Permeability, Porous media

Abstract

Pore network models are useful for studying transport in porous materials in a computationally efficient way. Extraction of networks from volumetric images has evolved over the years, starting with medial axis-based approaches to more recent watershed segmentation. This paper reconsiders the classic medial axis method, which offers several advantages such as speed and topological correctness, and develops a modernized, updated, and improved version. The new method is named Medial Axis Guided Network Extraction Tool (MAGNET). It works by analyzing the skeleton of a porous material to identify pore centers at junctions and endpoints. Additional pore bodies are found on long throats using two different approaches. This work includes an efficient tool for calculating the cross-sectional area of throats with irregular shape by using walkers with an infinite mean-free path to probe the geometry orthogonal to the medial axis at the point of the throat constriction. This extra step was critical for obtaining an equivalent diameter needed to calculate the permeability. Lastly, MAGNET was written with computational efficiency in mind. The skeletonization approach was itself 4.2X faster than the SNOW watershed segmentation for a 1000³ image. Additionally, a parallelized skeletonization was applied by processing the image in blocks with sufficient overlap which resulted in a 5.5X speed-up compared to the serial approach. To validate the output, MAGNET was tested on a 400³ voxel image of a Berea sandstone, and the flow and capillary properties of the extracted network were compared to the results from SNOW and the lattice-Boltzmann method. Structural information such as pore and throat size distribution and mercury intrusion curves was compared, and noticeable similarity was achieved. Crucially, the permeability predicted by MAGNET was within 5% of the lattice-Boltzmann prediction on the same image.

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