Premium
Efficient extraction of networks from three‐dimensional porous media
Author(s) -
Jiang Z.,
Wu K.,
Couples G.,
van Dijke M. I. J.,
Sorbie K. S.,
Ma J.
Publication year - 2007
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2006wr005780
Subject(s) - computer science , cluster analysis , topology (electrical circuits) , medial axis , computation , porous medium , algorithm , artificial neural network , artificial intelligence , porosity , mathematics , materials science , combinatorics , composite material
Fluid flow through porous media, and the thermal, electrical, and acoustic properties of these materials, is largely controlled by the geometry and topology (GT) of the pore system, which can be considered as a network. Network extraction techniques have been applied in many research fields, including shape representation, pattern recognition, and artificial intelligence. However, the set of algorithms presented here significantly improves the efficiency of common thinning algorithms by introducing a sufficiency condition based on the idea of a simple set. This paper describes an efficient and accurate algorithm for extracting the geometrical/topological network that represents the pore structure of a porous medium, referred to as the GT network. The accurate medial axis and the specific GT description of the network are achieved by applying symmetrical and interval strategies during the erosion step in the image processing. The GT network extraction algorithm presented here involves a number of steps, including (1) calculation of the three‐dimensional Euclidean distance map; (2) clustering of voxels; (3) extraction of the network of the pore space; (4) partitioning of the pore space; and (5) computation of shape factors. The focus of this paper is mainly on the thinning method that underpins points 1–3. The paper is primarily a method description, but we illustrate the functionality of the technique by extracting a pore scale GT network from micro–computer tomography images of three sandstones.