Image-based grain partitioning using skeleton extension erosion method
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KAUST DepartmentComputational Transport Phenomena Lab
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
KAUST Grant NumberBAS/1/1351-01
Online Publication Date2021-04-15
Print Publication Date2021-10
Embargo End Date2023-04-17
Permanent link to this recordhttp://hdl.handle.net/10754/668897
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AbstractImage-based grain partitioning is carried out to classify a granular rock sample's image into numerous single grains, which is the premise of obtaining grains' geometry features (e.g., grain size and sphericity) and grains' contact relationship. Because grain geometry features play an important role in understanding the rock petrophysical properties and the sedimentary environment, image-based grain partitioning attracts more and more attention in the digital core society. Several grain partitioning algorithms have been proposed and applied in some rock samples. However, it is still challenging to process the consolidated rock samples in which most of the grains adhered with each other, characterized by stylolite contact. All current grain partitioning algorithms contain two steps: grain center's extraction and identifying each grain domain. In the image of a granular rock sample, the grain centers can be defined as the local maxima of the solid phase's Euclidean distance map or the remaining cores after successive erosions of the solid phase. However, the former option prefers to result in an over partitioning, while the latter choice brings under partitioning. In this paper, an innovative image-based grain partitioning algorithm based on skeleton extension erosion strategy is proposed to improve the under partitioning problem. The key idea of the proposed method is to introduce the skeleton extension erosion strategy to realize the differential erosion by which the potential grain boundaries will be eroded but the other part is intact. This process effectively enhances the ability of the proposed method to deal with consolidated granular rock samples. The proposed method presents better performance than the popularly used methods, including the watershed grain partitioning method and the erosion-watershed grain partitioning method.
CitationWang, Y., & Sun, S. (2021). Image-based grain partitioning using skeleton extension erosion method. Journal of Petroleum Science and Engineering, 205, 108797. doi:10.1016/j.petrol.2021.108797
SponsorsThe two 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.