Abstract
Abstract. Representing fractures explicitly using a discrete fracture network (DFN) approach is often necessary to model the complex physics that govern thermo-hydro-mechanical–chemical processes (THMC) in porous media. DFNs find applications in modelling geothermal heat recovery, hydrocarbon exploitation, and groundwater flow. It is advantageous to construct DFNs from the photogrammetry of fractured outcrop analogues as the DFNs would capture realistic, fracture network properties. Recent advances in drone photogrammetry have greatly simplified the process of acquiring outcrop images, and there is a remarkable increase in the volume of image data that can be routinely generated. However, manually digitizing fracture traces is time-consuming and inevitably subject to interpreter bias. Additionally, variations in interpretation style can result in different fracture network geometries, which, may then influence modelling results depending on the use case of the fracture study. In this paper, an automated fracture trace detection technique is introduced. The method consists of ridge detection using the complex shearlet transform coupled with post-processing algorithms that threshold, skeletonize, and vectorize fracture traces. The technique is applied to the task of automatic trace extraction at varying scales of rock discontinuities, ranging from 100 to 102 m. We present automatic trace extraction results from three different fractured outcrop settings. The results indicate that the automated approach enables the extraction of fracture patterns at a volume beyond what is manually feasible. Comparative analysis of automatically extracted results with manual interpretations demonstrates that the method can eliminate the subjectivity that is typically associated with manual interpretation. The proposed method augments the process of characterizing rock fractures from outcrops.
Highlights
Fractured reservoir (NFR) modelling requires an explicit definition of fracture network geometry to accurately capture the effects of fractures on the overall reservoir behaviour
The use of deterministic discrete fracture networks (DFNs) based on trace digitization from the photogrammetry of outcrop analogues was investigated by Bisdom et al (2017) and Aljuboori et al (2015) for reservoir fluid flow simulation and well testing
Derived DFNs are based on the physics of fracture propagation (e.g. Olson et al, 2009; Thomas et al, 2018) and can reproduce realistic fracture patterns provided the complex paleostress field and paleo rock properties are known; they are computationally intensive and have limited applicability
Summary
Fractured reservoir (NFR) modelling requires an explicit definition of fracture network geometry to accurately capture the effects of fractures on the overall reservoir behaviour. The National Research Council (1996) suggested the idea of using geologically realistic outcrop fracture patterns to guide subsurface fracture modelling. Outcrop-derived DFNs encapsulate 2-D fracture network properties at a scale that cannot be characterized using either standard surface approaches (scanlines and satellite imagery) or subsurface techniques (seismic imaging/borehole imagery/core sampling). Stochastic and geomechanical DFNs are alternatives to outcrop-derived DFNs for fractured reservoir modelling. Generated DFNs have the disadvantage that they cannot replicate the spatial organization of fracture network patterns observed in nature (Thovert et al, 2017). False positives are non-geological features (such as trees, shrubbery, and human-made structures) that are detected using semiautomated or automated approaches (Vasuki et al, 2014). We review some approaches for automatic fracture detection based on the class of algorithm used
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