AUTOMATED RECONSTRUCTION OF FRACTURE NETWORKS FROM X-RAY MICROTOMOGRAPHY IMAGES

Abstract:

In the study of subsurface phenomena, fractures are encountered routinely. Fractures in rocks play a critical role in the rocks' strength, the stability of rock blocks, and the creation of flow pathways for fluids. Though fractures are ubiquitous in the subsurface, current understanding of the physics of transport through fractured porous media is severely limited by overly simplistic models. Discrete fracture networks (DFNs) are computational models that explicitly represent fracture network geometries and properties as either lines in 2D or planar polygons in 3D. These are based on the distribution of geometric properties, such as average aperture, density, and orientation whose accurate measurement is labor intensive and non-trivial in 3D. Therefore, current methods of modeling fractures and fracture-matrix flow mechanisms are difficult to validate with experimental data.

Currently, there are no fully automated algorithms available to construct an image-based fracture network based on a 3D image, such as X-ray microtomography. This is principally due to the lack of algorithms capable of reading a segmented image, extracting the individual fractures from the network and quantifying their geometric properties. The fundamental question is defining where one rough fracture start, and another end does, and we attempt to address this by combining advanced imaging and geostatistical tools to understand and model diverse geometries from 2D and 3D micro-computed tomography images of Mancos shale. The segmented volumes are the starting point to separate the fractures from the matrix. Then, unsupervised machine learning algorithms were implemented to cluster the points that belong to the same fracture. Finally, positional and geometric properties were extracted to serve as input data for the DFN software. The workflow proposed here can be a successful alternative to generate discrete fracture networks from micro-CT images.