A fuzzy logic based algorithm for defining and extracting pore network structure from tomography images of rocks

•The definition of a pore is vague and arbitrary. To make sense of pore network models and pore related quantities (pore size distribution, pore coordination number, throat size distribution), a quantifiable definition of a pore is needed.•We applied fuzzy logic to tackle the issue of quantifying th...

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Vydané v:Advances in water resources Ročník 119; s. 197 - 209
Hlavní autori: Moaddel, A., Müter, D., Gooya, R., Sørensen, H.O., Stipp, S.L.S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.09.2018
Elsevier Science Ltd
Predmet:
Algorithms
computed tomography
Fuzzy logic
geometry
Image processing
Image segmentation
Information entropy
Mathematical models
Medical imaging
Percolation
Pore network model
Pores
Porosity
Porous media
Properties
Properties (attributes)
Quality assessment
Rock
Rocks
Tomography
water resources
Watersheds
X ray imagery
Fuzzy logic
Porous media
Tomography
Pore network model
Percolation
Information entropy
ISSN:0309-1708, 1872-9657
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Popis
Shrnutí:•The definition of a pore is vague and arbitrary. To make sense of pore network models and pore related quantities (pore size distribution, pore coordination number, throat size distribution), a quantifiable definition of a pore is needed.•We applied fuzzy logic to tackle the issue of quantifying the pore as an entity in a porous medium and tested it on a variety of rock types, using tomography images.•By using a plot of information content of pore coordination number distribution, it is possible to cluster different rocks. X-ray tomography has opened a window into the invisible world of pore structure in rocks. Using tomography images as input, pore network models try to use a simple geometric representation to acquire a detailed description of the pore volume. Algorithms developed so far to extract the pore network from tomography images are primarily focused on calculating petrophysical properties of the rock sample. The present study is concerned with extracting a pore network that enables us to elicit information about structural properties and features of the rock sample. However, there is no quantifiable definition of what a pore is and deciding how to divide the pore volume into discrete pores is vague and arbitrary. Fuzzy logic is an extension of classical logic that can accommodate situations where the nature of problem itself is vague. We have developed an algorithm to extract the pore structure and quantify the pore properties from tomography images of rocks. In the algorithm a fuzzy logic based inference engine is used to assess the quality of pores extracted by watershed segmentation and modify them if needed. We have also designed a fingerprinting plot based on the information content of the network, to quantify performance of the algorithm. Results show that the algorithm is able to link the overall structure of the porous space to the final network and produce reliable results for rocks with a variety of pore structures.
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ISSN:0309-1708
1872-9657
DOI:10.1016/j.advwatres.2018.07.011