The Lilstock outcrop in the southern Bristol Channel provides exceptional exposures of several limestone beds displaying stratabound fracture networks, providing the opportunity to create a very large, complete, and ground-truthed fracture model. Here we present the result of automated fracture extraction of high-resolution photogrammetric images (0.9 cm/pixel) of the full outcrop, obtained using an unmanned aerial vehicle, to obtain a spatially extensive, full-resolution map of the complete fracture network with nearly 350,000 ground-truthed fractures. We developed graph-based functions to resolve some common issues that arise in automatic fracture tracing such as incomplete traces, incorrect topology, artificial fragmentation, and linking of fracture segments to generate geologically significant trace interpretations. The fracture networks corresponding to different regions within the outcrop are compared using several network metrics and the results indicate both inter- and intra-network (layer to layer) structural variabilities. The dataset is a valuable benchmark in the study of large-scale natural fracture networks and its extension to stochastic network generation in geomodelling. The dataset also highlights the intrinsic spatial variation in natural fracture networks that can occur even in weakly-deformed rocks over relatively short length scales of tens of metres.

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Rock fractures organize as networks, exhibiting natural variation in their spatial arrangements. Therefore, identifying, quantifying, and comparing variations in spatial arrangements within network geometries are of interest when explicit fracture representations or discrete fracture network models are chosen to capture the influence of fractures on bulk rock behaviour. Treating fracture networks as spatial graphs, we introduce a novel approach to quantify spatial variation. The method combines graph similarity measures with hierarchical clustering and is applied to investigate the spatial variation within large-scale 2-D fracture networks digitized from the well-known Lilstock limestone pavements, Bristol Channel, UK. We consider three large, fractured regions, comprising nearly 300 000 fractures spread over 14 200 m2 from the Lilstock pavements. Using a moving-window sampling approach, we first subsample the large networks into subgraphs. Four graph similarity measures – fingerprint distance, D-measure, Network Laplacian spectral descriptor (NetLSD), and portrait divergence – that encapsulate topological relationships and geometry of fracture networks are then used to compute pair-wise subgraph distances serving as input for the statistical hierarchical clustering technique. In the form of hierarchical dendrograms and derived spatial variation maps, the results indicate spatial autocorrelation with localized spatial clusters that gradually vary over distances of tens of metres with visually discernable and quantifiable boundaries. Fractures within the identified clusters exhibit differences in fracture orientations and topology. The comparison of graph similarity-derived clusters with fracture persistence measures indicates an intra-network spatial variation that is not immediately obvious from the ubiquitous fracture intensity and density maps. The proposed method provides a quantitative way to identify spatial variations in fracture networks, guiding stochastic and geostatistical approaches to fracture network modelling.@en

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 10° to 102m. 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.

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In this paper, we present a two-dimensional numerical model for modelling of hydraulic fracturing in anisotropic media. The numerical model is based on extended finite element method. The saturated porous medium is modelled using Biot’s theory of poroelasticity. An enhanced local pressure model is used for modelling the pressure within the fracture, taking into account the external fluid injection and the leak-off. Directional dependence of all the rock properties, both elastic and flow related, is taken into account. A combination of the Tsai–Hill failure criterion and Camacho–Ortiz propagation criterion is proposed to determine the fracture propagation. We study the impact on fracture propagation (in both magnitude and direction) due to anisotropies induced by various parameters, namely ultimate tensile strength, Young’s modulus, permeability and overburden pressure. The influence of several combinations of all these anisotropies along with different grain orientations and initial fracture directions on the fracture propagation direction is studied. Different regimes are identified where the fracture propagation direction is controlled by the degree of material anisotropy instead of the stress anisotropy.

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In this paper, we present a two dimensional model for modelling the hydraulic fracture process in anisotropic and heterogeneous rocks. The model is formulated using extended finite elements (XFEM) in combination with Newton-Raphson method for spatial and Euler's implicit scheme for time. The fracture is modelled with the help of cohesive zone method (CZM). Anisotropy arising due to orientation of grains in a specific direction in rocks is modelled to understand the influence of the degree of anisotropy and the grain orientation direction on fracture propagation. A combination of Tsai-Hill failure criterion and Camacho-Ortiz propagation criteria is used to predict mixed mode fracture propagation in anisotropic media. Effect of heterogeneities due to material inclusions, on fractures are modelled.@en

Electromagnetic waves are generated by acoustic waves at interfaces between fluids and fluid-saturated porous media, and at porous medium interfaces between different fluids. These signals can be measured in the laboratory as predicted by theory. A potential application is oil spill remediation monotoring in soils. We designed and developed an experimental setup in which acoustic to electromagnetic (EM) wave conversions at interfaces can be measured. Theoretical results are obtained with an electrokinetic full-waveform theoretical model, where use was made of the Sommerfeld approach. Using bimodal samples, different fluid–solid interface effects and saturating fluids were investigated. The contrast between water and water-saturated porous glass samples is larger than the contrast between water and oil-saturated porous glass samples. The contrast between water and water-saturated Fontainebleau sandstone is larger than the contrast between oil and water-saturated Fontainebleau sandstone.@en