Digifrac: Reconstruction and quantification of discrete fractures in rocks using micro-CT images

Fractures in rock masses are a central focus in research areas such as unconventional energy extraction, nuclear waste disposal, and carbon sequestration. Laboratory investigations of fracture parameters are essential for optimizing field operations. In recent years, CT scanning has emerged as a widely adopted non-destructive inspection technique. However, existing methods for post-processing CT scan data face persistent challenges in achieving high accuracy and efficiency. To address these challenges, we propose a novel Python-based post-processing framework that integrates a slice-by-slice thinning algorithm, local thickness computation, and point cloud data processing techniques. This framework enables precise characterization of fractured digital rocks by quantifying fracture width distribution and fracture surface orientation, alongside standard structural evaluation metrics such as the fractal dimension, volume ratio, and the H-index. Its feasibility, accuracy, and flexibility are validated through analyses of diverse fracturing samples, including fluid-fractured samples, shear-induced fracture samples, and samples containing multiple secondary fractures.