A rock fabric classification method based on the grey level co-occurrence matrix and the Gaussian mixture model

Yuzhu Wang; Shuyu Sun

doi:10.1016/j.jngse.2022.104627

A rock fabric classification method based on the grey level co-occurrence matrix and the Gaussian mixture model

Yuzhu Wang, Shuyu Sun

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Accurate classification of the rock fabric plays a crucial role in revealing the heterogeneity of the reservoir at different scales. This paper proposes an image-based rock fabric classification method using grey level co-occurrence matrix (GLCM) properties and Gaussian Mixture Model (GMM) as texture descriptors and classifier, respectively. The proposed method is successfully used to classify the images with heterogeneous pore structures and the pictures of outcrops with different sedimentary beddings without preparing the training dataset. According to our results, the classification performance decreases along with the increase of the number of fabric types and the decrease of the structure contrast among different rock types.

Original language	English (US)
Pages (from-to)	104627
Journal	Journal of Natural Gas Science and Engineering
DOIs	https://doi.org/10.1016/j.jngse.2022.104627
State	Published - May 27 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-06-01
Acknowledged KAUST grant number(s): URF/1/3769–01, URF/1/4074–01, BAS/1/1351–01
Acknowledgements: The authors would like to thank the King Abdullah University of Science and Technology (KAUST) for the funding support (under Grants No: URF/1/3769–01, URF/1/4074–01, and BAS/1/1351–01) and supercomputing resources from the Supercomputing Laboratory.

ASJC Scopus subject areas

Energy Engineering and Power Technology

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10.1016/j.jngse.2022.104627

https://repository.kaust.edu.sa/bitstream/10754/678351/1/1-s2.0-S1875510022002153-main_removed.pdf

Cite this

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title = "A rock fabric classification method based on the grey level co-occurrence matrix and the Gaussian mixture model",

abstract = "Accurate classification of the rock fabric plays a crucial role in revealing the heterogeneity of the reservoir at different scales. This paper proposes an image-based rock fabric classification method using grey level co-occurrence matrix (GLCM) properties and Gaussian Mixture Model (GMM) as texture descriptors and classifier, respectively. The proposed method is successfully used to classify the images with heterogeneous pore structures and the pictures of outcrops with different sedimentary beddings without preparing the training dataset. According to our results, the classification performance decreases along with the increase of the number of fabric types and the decrease of the structure contrast among different rock types.",

author = "Yuzhu Wang and Shuyu Sun",

note = "KAUST Repository Item: Exported on 2022-06-01 Acknowledged KAUST grant number(s): URF/1/3769–01, URF/1/4074–01, BAS/1/1351–01 Acknowledgements: The authors would like to thank the King Abdullah University of Science and Technology (KAUST) for the funding support (under Grants No: URF/1/3769–01, URF/1/4074–01, and BAS/1/1351–01) and supercomputing resources from the Supercomputing Laboratory.",

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doi = "10.1016/j.jngse.2022.104627",

language = "English (US)",

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journal = "Journal of Natural Gas Science and Engineering",

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AU - Wang, Yuzhu

AU - Sun, Shuyu

N1 - KAUST Repository Item: Exported on 2022-06-01 Acknowledged KAUST grant number(s): URF/1/3769–01, URF/1/4074–01, BAS/1/1351–01 Acknowledgements: The authors would like to thank the King Abdullah University of Science and Technology (KAUST) for the funding support (under Grants No: URF/1/3769–01, URF/1/4074–01, and BAS/1/1351–01) and supercomputing resources from the Supercomputing Laboratory.

PY - 2022/5/27

Y1 - 2022/5/27

N2 - Accurate classification of the rock fabric plays a crucial role in revealing the heterogeneity of the reservoir at different scales. This paper proposes an image-based rock fabric classification method using grey level co-occurrence matrix (GLCM) properties and Gaussian Mixture Model (GMM) as texture descriptors and classifier, respectively. The proposed method is successfully used to classify the images with heterogeneous pore structures and the pictures of outcrops with different sedimentary beddings without preparing the training dataset. According to our results, the classification performance decreases along with the increase of the number of fabric types and the decrease of the structure contrast among different rock types.

AB - Accurate classification of the rock fabric plays a crucial role in revealing the heterogeneity of the reservoir at different scales. This paper proposes an image-based rock fabric classification method using grey level co-occurrence matrix (GLCM) properties and Gaussian Mixture Model (GMM) as texture descriptors and classifier, respectively. The proposed method is successfully used to classify the images with heterogeneous pore structures and the pictures of outcrops with different sedimentary beddings without preparing the training dataset. According to our results, the classification performance decreases along with the increase of the number of fabric types and the decrease of the structure contrast among different rock types.

UR - http://hdl.handle.net/10754/678351

UR - https://linkinghub.elsevier.com/retrieve/pii/S1875510022002153

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DO - 10.1016/j.jngse.2022.104627

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JO - Journal of Natural Gas Science and Engineering

JF - Journal of Natural Gas Science and Engineering

ER -

A rock fabric classification method based on the grey level co-occurrence matrix and the Gaussian mixture model

Abstract

Bibliographical note

ASJC Scopus subject areas

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