Estimating anisotropy of fracture patterns using gray level Co-occurrence matrix (GLCM) approach: Implication on understanding permeability anisotropy

Fractures are the main pathways for fluid flow in reservoir rocks with low matrix permeability. Natural fractures are typically anisotropic, causing fluid flow to vary by direction. Accurate characterization of this anisotropy is essential for predicting reservoir behaviour and performance. This study applies a second-order statistical method—Gray-Level Co-occurrence Matrix (GLCM) analysis—to quantify fracture network anisotropy. Grayscale fracture images were examined in horizontal (0° East) and vertical (90° North) directions, and textural anisotropies were computed for key GLCM features: Contrast, Dissimilarity, Homogeneity, Energy, and Entropy. Permeability anisotropy was further estimated through numerical simulations, and its correlation with GLCM-based textural anisotropy was evaluated for both natural and synthetic fracture patterns.

Results show that GLCM-based textural anisotropy captures not only pixel-level directional intensity variations but also is sensitive to the variation in underlying fracture geometrical attributes—orientation, density, aperture variation, and length distribution—that control directional flow. Across all datasets, permeability anisotropy correlated positively with Homogeneity and Energy anisotropies, and negatively with Contrast, Dissimilarity, and Entropy anisotropies. Among these, Entropy-anisotropy consistently emerged as the strongest predictor of permeability anisotropy. Applying this method to natural fracture networks, including fault damage zones, confirmed that GLCM-based textural anisotropy can non-invasively reveal fracture network anisotropy governing directional permeability. This approach has potential applications in reservoir characterization, hydrogeological modelling, and geothermal resource assessment.