Image-based rock typing using local homogeneity filter and Chan-Vese model
KAUST DepartmentApplied Mathematics and Computational Science Program
Computational Transport Phenomena Lab
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2021-02-06
Print Publication Date2021-05
Embargo End Date2023-03-13
Permanent link to this recordhttp://hdl.handle.net/10754/668177
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AbstractImage-based rock typing is carried out to classify an image of the heterogeneous rock sample into different rock types where each rock type can be treated as a homogeneous porous medium. In this study, we propose an innovative method for rock typing of the heterogeneous rock sample via three steps. First, the target image, a segmented binary image with two phases of pore and solid, is consecutively inputted into two filters of a local homogeneity filter and an average filter to increase the contrast between different rock types and decrease the contrast within each single rock type. Second, Chan-Vese model is applied to classify the filtered image into different rock types. Third, a thresholding is used to remove the particles, which are treated as noisy particles, smaller than a given preset size. The main idea of the local homogeneity filtering introduced in this study is undertaken by counting the number of pixels that possess the same phases as the center pixel within a 3 × 3 pixels neighborhood. This process is carried out iteratively, which means the previously estimated pixel will be used in the estimation of its neighbor unprocessed pixels. We demonstrate the application of the proposed method in several heterogeneous images and present good performance.
CitationWang, Y., Alzaben, A., Arns, C. H., & Sun, S. (2021). Image-based rock typing using local homogeneity filter and Chan-Vese model. Computers & Geosciences, 150, 104712. doi:10.1016/j.cageo.2021.104712
SponsorsThe four authors cheerfully acknowledge that this work is supported by King Abdullah University of Science and Technology (KAUST) through the grants BAS/1/1351-01, URF/1/4074-01, and URF/1/3769-01. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.
JournalComputers & Geosciences
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