FracDetect: A novel algorithm for 3D fracture detection in digital fractured rocks

Abstract

Fractures have a governing effect on the physical properties of fractured rocks, such as permeability. Accurate representation of 3D fractures is, therefore, required for precise analysis of digital fractured rocks. However, conventional segmentation methods fail to detect and label the fractures with aperture sizes near or below the resolution of 3D micro-computed tomographic (micro-CT) images, which are visible in the greyscale images, and where greyscale intensity convolution between different phases exists. In addition, conventional methods are highly subjective to user interpretation. Herein, a novel algorithm for the automatic detection of fractures from greyscale 3D micro-CT images is proposed. The algorithm involves a low-level early vision stage, which identifies potential fractures, followed by a high-level interpretative stage, which enforces planar continuity to reject false positives and more reliably extract planar fractures from digital rock images. A manually segmented fractured shale sample was used as the groundtruth, with which the efficacy of the algorithm in 3D fracture detection was validated. Following this, the proposed and conventional methods were applied to detect fractures in digital fractured coal and shale samples. Based on these analyses, the impact of fracture detection accuracy on the analysis of fractured rocks' physical properties was inferred.