Frontiers in Earth Science (Nov 2020)
Three-Dimensional Separation and Characterization of Fractures in X-Ray Computed Tomographic Images of Rocks
Abstract
Open fractures can affect petrophysical properties of their host rock masses, as well as fluid transport and storage, so characterization of them is important to both industrial and research scientists. X-ray Computed Tomography (CT), a non-destructive technique for 3D imaging of various materials, shows such fractures well in rock samples. However, separation and characterization of fractures in CT data is complicated when a scanned sample contains narrow and intersecting fractures, because narrow fractures become blurred when thinner than the scanner resolution and their value approximates that of the matrix, and because intersecting features are difficult to individually characterize. In this paper, we present a new approach for an objective and efficient characterization of the fracture network inside CT scans of rock samples. We have developed algorithms, implemented as Python scripts, that measure fracture aperture-related parameters, and that separate connected fractures and fracture intersections within CT images of the sample. The CT images are composed of stacks of 2D images in the plane parallel to X-Y (equally spaced), where each pixel has a value related to the attenuation of the X-rays within the materials that make up the sample at that location and is generally displayed using a gray-scale colormap. As the gray values in the reconstructed images drop within fractures, our algorithm is able to identify such drops and record the lowest gray value in every drop as a Fracture Trace Point (FTP). For every FTP, parameters related to the local fracture width and the three-dimensional orientation of the FTPs surrounding it are measured. A second step involves the separation of individual fractures and their intersections points. This allows information about a number of FTP measurements on the same fracture (or intersections) to be combined to characterize that feature. We demonstrate that our methods better quantify fractures and their intersections through analysis of an experimentally-deformed granite sample, within which we characterize fracture size, orientation, and intensity. The methodologies can be also used to characterize sub-planar features in other types of datasets. Python implementations of our algorithms are freely available on GitHub repositories.
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