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 the one 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 core 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 quantify fractures and their intersections in high detail 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.
Highlights
Open fractures are three-dimensional (3D) structures that are typically planar-like
Partial Volume Effect (PVE) is a consequence of the true resolution of the scanner, which can be described in one- and two-dimensions by measuring the Line Spread Function (LSF) or Edge Response (ER), and the Point Spread Function (PSF), respectively (Smith, 1997; Smith, 2003; Ketcham, 2006)
We have demonstrated the individual and combined capabilities of the Fracture Trace Point (FTP) and fracture separation approaches to characterize the fracture network in a trial dataset
Summary
Open fractures are three-dimensional (3D) structures that are typically planar-like. The fracture walls typically have rough and irregular surfaces that strongly affect how natural fluids can flow through or be stored in them. Fluids flow through them on convoluted paths that follow the minimum resistance generated by the local pressure gradients, which strongly depend on the local fracture aperture and roughness (Liu et al, 2016; Luo et al, 2016; Makedonska et al, 2016; Zambrano et al, 2019) Number of fractures, their sizes and geometries impact generation of space and connections the storage and transmissivity of fluids (Long and Witherspoon, 1985; Hyman et al, 2016; Liu et al, 2016; Makedonska et al, 2016; March et al, 2018). To truly characterize these fracture properties, it would be best to observe them in their undisturbed state through the surrounding rock mass – in other words to image their geometry in 3D rather than physically breaking them apart.
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