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Manuscript_Rock_Typing_GP.pdf
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15.54Mb
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Description:
Accepted Manuscript
Embargo End Date:
2023-01-01
Type
ArticleAuthors
Wang, YuzhuSun, Shuyu

KAUST Department
Physical Science and Engineering (PSE) DivisionEarth Science and Engineering Program
KAUST Grant Number
BAS/1/1351-01URF/1/3769-01
URF/1/4074-01
Date
2021-01Embargo End Date
2023-01-01Submitted Date
2020-03-22Permanent link to this record
http://hdl.handle.net/10754/667444
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Show full item recordAbstract
Image-based rock typing is carried out to quantitatively assess the heterogeneity of the reservoir specimen at a pore scale by classifying an image of a heterogeneous rock sample into a number of relatively homogeneous regions. Image-based rock typing can be treated as a special application of texture classification in the field of the digital core. In conventional texture classification algorithms, a single size window or a set of windows with different size are applied to scan the image to extract various local structure features, and then a classification algorithm is used to classify the image into different regions where each region possesses unique structure features. Due to the local features are extracted within a window, it is still challenging to identify the class of the voxels close to the boundary between different regions. In this paper, a rock typing method is proposed, which uses the geometry features of the grains instead of local structure features for classification. Inspired by the fact that in some cases the heterogeneity of the reservoir is mainly affected by the sedimentary process, which means each rock type always has certain specific grain features such as size and sphericity. To this kind of rock samples, the proposed grain-based rock typing algorithm can effectively address the boundary ambiguousness problem. In this study, the grains of the rock sample are partitioned firstly, and then their geometry features are calculated. Then a support vector machine algorithm is used to classify these grains into different rock types. Finally, the pore voxels are given a rock type, which is identical to its nearest grain. The proposed method shows excellent performance in the heterogeneous samples whose grains are available to be partitioned and distinguishable.Citation
Wang, Y., & Sun, S. (2021). Image-based rock typing using grain geometry features. Computers & Geosciences, 104703. doi:10.1016/j.cageo.2021.104703Sponsors
The 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.Publisher
Elsevier BVJournal
Computers & GeosciencesAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S0098300421000182Relations
Is Supplemented By:- [Software]
Title: yuzhu561/digitalgeo: This code is developed for image-based rock typing using the geometry features of grains. Publication Date: 2020-04-15. github: yuzhu561/digitalgeo Handle: 10754/668350
ae974a485f413a2113503eed53cd6c53
10.1016/j.cageo.2021.104703