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) |
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Pages (from-to) | 104627 |
Journal | Journal of Natural Gas Science and Engineering |
DOIs | |
State | Published - May 27 2022 |
ASJC Scopus subject areas
- Energy Engineering and Power Technology