In this study, we present a method that uses associations of discontinuity sets to demonstrate similarities between the outcrop and the subsurface. A discontinuity association comprises up to four discontinuity sets (fractures and stylolites) that can form coeval in a single stress field, a well-known concept that is rarely applied for subsurface characterization of discontinuities. We use this concept to improve the interpretation of borehole image logs of naturally fractured geothermal reservoirs in the Geneva Basin, Switzerland. Here, the naturally fractured Lower Cretaceous pre-foredeep carbonate rocks are targeted for geothermal exploitation, and exposures of this formation are found in three mountain ranges that surround the basin. In these outcrops, the orientations of the discontinuity associations are used as paleostress indicators in order to map out principal stress trajectories of regional discontinuity-forming events that created the background discontinuity network. We document two multiscale discontinuity-forming events that formed prior to Alpine fold-and-thrusting and thus constitute the regional-scale background network. Given the regional character of these events, we predict that the target reservoir is impacted by them as well. This prediction is subsequently used to isolate the background-related discontinuities on image logs from two boreholes that penetrate the target reservoir in the Geneva Basin. This analysis reveals that ∼ 45 % of the observed discontinuities can be understood in the framework of the regional-scale background. In this way, we demonstrate that defining discontinuity associations in outcrops is a powerful tool to predict the geometry of natural discontinuity networks in the subsurface and subsequently can be used to develop geothermal exploitation strategies in naturally fractured reservoirs.

Natural discontinuity networks control convective fluid flow in carbonate geothermal reservoirs with low matrix porosity and permeability. The network can be separated into discontinuities that formed due to local drivers (e.g. faults/folds) and the background network formed by far-field stresses, each with different scaling behaviour. Borehole data are the only source to sample the subsurface network, as the majority of the discontinuities are of sub-seismic scale. Borehole images are the most cost-effective way of sampling the network, but the limited sample area and image resolution hamper the identification of the background network in this dataset. Analogue outcrops may complement the borehole data, but only after the analogy between outcrop and subsurface reservoir is established. In this study, we present a method that uses associations of discontinuity sets to establish a robust link between the outcrop and the subsurface. A discontinuity association comprises up to 4 discontinuity sets that can form coeval in a single stress field, a well-known concept that is rarely applied for subsurface characterization of discontinuities. We use the orientations and type of discontinuity associations as paleostress indicators in order to map out principal stress trajectories of regional discontinuity-forming events that created the background discontinuity network. We demonstrate this methodology in the Geneva Basin, Switzerland, where the naturally fractured Lower Cretaceous pre-foredeep carbonates are targeted for geothermal exploitation. Outcrops in the mountain ranges that surround the basin, consistently reveal two multiscale discontinuity-forming events that formed prior to Alpine fold-and-thrusting and thus constitute the regional scale background network. Therefore, based on the analogy principle, we predict that the target reservoir is also affected by these events. We use this prediction to isolate background-related discontinuities on image logs from two borehole that penetrate the target reservoir in the Geneva Basin. This analysis reveals that ∼45 % of the observed discontinuities can be understood in the framework of the regional-scale background. In this way, we demonstrate that DAs in outcrops are a powerful tool to predict the geometry of natural discontinuity networks in the subsurface and subsequently can be used to develop geothermal exploitation strategies in naturally fractured reservoirs.

To enable reliable exploration strategies for geothermal energy that have inherently lower economic and technical risks and hence increase public support, the multi-national, multi-disciplinary, and publicly funded FindHeat project is developing a novel, conceptual model-based geothermal exploration workflow. This workflow specifically focuses on faster turnaround times for exploration and appraisal of geothermal resources, making better use of legacy data and non-invasive geophysical techniques, and constraining uncertainties with respect to the size of the heat source and the range of possible heat production rates. Comprehensive social science research complements the technical work to set the foundation for new communication strategies that allow geothermal operators to earn the public trust that improved geothermal exploration and appraisal will lead to a more efficient and sustainable exploitation of geothermal energy. The workflow is being tested and validated at eight geologically diverse geothermal plays situated in Iceland, France, UK, Spain, and Netherlands, which allows us to demonstrate its economic and technical benefits as well as its societal impact.

Evaluating the geothermal reservoir potential often requires fracture analysis, as fractures serve as key pathways for fluid flow in subsurface formations. Borehole images (BHIs) are essential for this analysis, providing 2D representations of boreholes with millimetre-scale resolution. However, their interpretation is highly subjective, leading to uncertainties in the results and the subsequent quantitative assessment of the fracture networks. In the West Netherlands Basin (WNB), accurate fracture characterization is critical for assessing the geothermal viability. However, the traditional manual interpretation of BHIs has shown inconsistencies. This study introduces a supervised deep learning (DL) approach to support fracture analysis using high-resolution formation micro-imager (FMI) data from the Naaldwijk well (NLW-GT-01). The proposed DL-based system integrates a U-Net model (PickNet) for segmentation and a fully connected convolutional network (FitNet) for automated feature extraction. Initially trained on synthetic low-resolution BHIs, the model has been adapted for FMI data using two approaches: (1) transfer learning and (2) a simplified adaptation method that involves resizing the FMI input, leading to some resolution loss. A comparison of these approaches has revealed that the simplified adaptation produces better results, closely aligning with conservative manual interpretations calibrated with core samples while enabling more detailed fracture detection. To enhance reliability, we propose a semi-automated human–machine collaboration framework, where experts validate or refine the automatically detected features. This approach leverages human expertise to improve interpretation accuracy while addressing challenges related to robustness and redundancy in the supervised learning model.

High technical and economic risks stemming from the lack of detailed knowledge of the subsurface hold back large-scale investments in geothermal energy. In a survey conducted on nine use cases from diverse geological settings across Europe and with different purposes (electricity/heating and cooling) and project objectives (scientific/commercial), we identify the “common practice” and the aspiration for the “state of the art” in geothermal exploration. For each use case, the survey investigates what workflows have been adopted and what data acquired by which methods at different stages of exploration. This provided a benchmark for exploration in a range geothermal play types. The survey shows that this industry-standard base-case can be adapted to improve exploration success and efficiency by (1) applying numerical modelling in early stages of exploration to guide strategic data collection, (2) novel application of innovative technologies and (3) closer integration of software tools for static geological interpretation and dynamic heat flow simulation.

Characterising fractures in geothermal reservoirs is crucial for understanding heat and fluid flow, as fractures control reservoir permeability. Due to data scarcity, estimating fracture network properties remains uncertain. Dynamic data, such as well tests, provides indirect insights into subsurface properties and workflows have been developed to illustrate how uncertainty in fracture data affects flow behaviour. However, they use simplified, randomly generated fracture geometries limiting their applicability to real-world scenarios. This study presents a machine learning workflow for characterizing fractured reservoirs using transient data, focusing on geothermal reservoirs. A comprehensive dataset of 5000 geologically consistent Discrete Fracture Networks (DFNs) was generated using GeoDFN and directly linked to MRST for simulations. The workflow then applies a k-medoids clustering approach, using dynamic time warping (DTW) as a distance metric, to cluster pressure responses with similar transient behaviour. We identified 18 distinct pressure behaviour. Linking clusters to fracture properties reveals that fracture intensity, aperture, and length have the most significant impact on pressure behaviour, while fracture set type was found to be the least important factor. Future work will extend this workflow to temperature transient data and apply advanced machine learning techniques for both forward and inverse modelling of fractured geothermal reservoirs.

Fractures are ubiquitous in geological formations and can often have an impact on subsurface applications such as geothermal energy, groundwater management or CO2 storage. Quantifying the relationship between the uncertainties inherent to fracture networks and the corresponding flow behaviour for these applications remains an open challenge. Simulation studies that are based on outcrop analogues of fracture networks have yielded many new insights about heat and mass transfer in fractured geological formations but are restricted to a limited number of fracture network realizations, simplified assumptions about fracture network properties or deterministic models, making it difficult to analyse a wide range of uncertainties. This study introduces a flexible workflow that generates ensembles of geologically plausible fracture networks that can be based on statistical data from outcrop analogues. The fracture networks are generated using a computationally efficient approach that combines mechanical and statistical methods. The ensembles are then seamlessly linked to multi-purpose flow and transport simulations where the fractures are represented explicitly in a porous and permeable rock matrix. This approach can enable new uncertainty quantification methods, supported by machine-learning-based emulators, to analyse how fracture network properties, such as fracture intensity, fracture aperture or fracture orientation, influence heat and mass transfer in fractured geological formations. The workflow is illustrated using two classic example applications pertinent to fracture network modelling – one based on outcrop data to assess thermal behaviour in geothermal systems, and one synthetic study to analyse the transition from matrix-dominated to fracture-dominated flow – and released as open-source code.

Fracture networks play a critical role in fluid flow within reservoirs, and it is therefore important to understand the interactions and influences of these networks. Our study focuses on the Southern Chotts–Jeffara Basin, which hosts reservoirs within Triassic, Permian and Ordovician units containing significant hydrocarbon accumulations. Recent developments on the structural understanding of the basin have proved that a regional shortening phase occurred between the Permian and Jurassic, forming open folds and a distributed fracture network. Analysis of late Paleozoic and Mesozoic outcrops within the basin has identified several sets of fractures (with dip directions and dip angles of 150/80 and 212/86) and compressional structural features that support this shortening hypothesis. We have integrated fracture data from surface analogues and subsurface analysis of advanced seismic attributes and well data through structural linking to form a 2D hybrid fracture model of the reservoirs in the region. Through analytical aperture modelling and numerical simulation, we found that the fractures orientated 212° in combination with large-scale fractures contribute significantly to the fluid-flow orientation and potential reservoir permeability. Our presented fracture workflow and framework provide an insight into network characterization within naturally fractured reservoirs of Tunisia, and how certain structures form fluid pathways that influence flow and production.

The lower Triassic Main Buntsandstein Subgroup represents a promising, but high-risk geothermal play in the Netherlands. Although the gross thickness in boreholes locally exceeds 200 m, the spatial distribution, geometries and preservation of these sedimentary units remained uncertain due to the lack of seismic data with sufficient resolution and the sparse borehole network. This creates uncertainty in the quantification of the aquifer dimensions that is essential for the planning of geothermal operations.

In this study, seismic interpretation and 2D palinspastic restoration of new and reprocessed seismic data were conducted and combined with borehole data to assess the tectonic evolution of the Roer Valley Graben in the southeastern Netherlands and its control on the spatial distribution of the Main Buntsandstein Subgroup sediments. Our results show that the central and southern parts of the Roer Valley Graben were active depocenters in the Early to Middle Triassic times dominated by fluvial sandstone deposition, providing important play elements for prospective leads on geothermal exploration. The northern part of the basin was a more marginal area where mostly fine-grained sediments were deposited. To the northwest, differential subsidence resulted in the development of areas where the Buntsandstein thickness is reduced to ∼150 m.

After deposition, the Main Buntsandstein sediments were compartmentalised by faulting related to post-depositional tectonic activity, locally reducing the lateral extent of the geothermal target areas down to 1–2 km in a ∼NE–SW direction. On the platform areas adjacent to the Roer Valley Graben and to the southeast, Jurassic sediments are largely absent and the Main Buntsandstein sediments are present at depths shallower than 2 km. These platforms are promising targets for further investigation, as the relatively shallow burial depths, compared to the central part of the Graben, may have contributed to the preservation of more favourable reservoir properties.

Southern Tunisia is known to be less deformed and simpler than its neighboring Atlassic domain to the north. This area is complex and basin evolution in the Southern Chotts-Jeffara (SCJ) basin is debated. In this paper we combined surface and subsurface data with low temperature thermochronology (LTT) to reinvestigate the tectono-sedimentary evolution of the SCJ basin from Permian to Jurassic. We reconstruct the present-day architecture of the SCJ basin along two regional sections. In these sections, we focused mainly on regional thickness variations and on internal reflections interpreted from seismic data. We observe three structural elements: (a) A Paleozoic culmination, oriented E-W, capped by Mid-Upper Triassic deposits; (b) the Tebaga of Medenine (ToM), a culmination also oriented E-W but located ∼50 km north of the Paleozoic culmination; and (c) A Triassic culmination in the eastern part of the area, oriented NW-SE. We note the absence of major normal faults along the sections. The LTT data we present are the first published in this area and allow to reconstruct the timing and magnitude of vertical movements. These data prove: (a) exhumation at ∼230 Ma of the Permian and Lower Triassic units associated with the onset of the ToM removing locally about 900 m of pre-Cretaceous sediments; and (b) the development of the Triassic culmination ∼180 Ma removing 2000 m of pre-Cretaceous sediments in the Jebel Rehach. This study demonstrates that vertical movements in the SCJ basin are controlled by long-wavelength processes developed essentially in shortening regimes.

The heterogeneity of the Upper Jurassic carbonate reservoir (Malm reservoir) beneath the North Alpine Foreland Basin has a significant influence on the mass and heat flow processes during geothermal exploitation. Geophysical borehole data revealed that sub-seismic scale fractures and karstified fractures occur at the inflow zones of deep geothermal wells. However, pressure transient analysis (PTA) in some previous studies concluded that it is difficult to detect the influence of sub-seismic scale features, suggesting that radial flow regime is dominant. Accordingly, a regional thermal-hydraulic model adopted the equivalent porous medium (EPM) approach, homogenizing the sub-seismic scale reservoir heterogeneities; however, unable to detect an early thermal breakthrough (ETB) in a geothermal doublet located SE of Munich. We apply PTA on three buildup tests belonging to that doublet following a deterministic approach to constrain the reservoir type by interpreting the pressure derivative (PD) plots constrained by geophysical and geological data. We derive the magnitudes of the reservoir hydraulic parameters by matching the PD plots with the selected interpretation models. We find that clustered fractures have a significant influence on the reservoir hydraulics, evidenced by trough-shaped curves in the PD plots. Linear flow regime interpreted from the interference test between the two wells indicates permeability anisotropy, which may have caused the ETB. Geophysical data interpretations indicate that these fractures correspond to a coupled fault damage zone and a fracture corridor. Finally, we present a fit-for-purpose 2D discrete fracture network model utilizing the PTA results to match our analytically calibrated model. Our study offers a potential hydraulic explanation to the cause of the ETB highlighting the importance of integrating multi-scale/disciplinary data sets to improve the reliability of dynamic reservoir models, based on which, economic-related decisions are made.

In this study, a re-evaluation is performed of the well data of the NLW-GT-01 and VAL-01 wells in the Lower Triassic sandstones in the West Netherlands Basin. Core, geophysical and image logs, are compared to document the characteristics of natural fractures distribution, and investigate the geological parameters influencing their development. The main control on the distribution is identified. Natural fractures are concentrated in heterolithic lithological intervals of the VAL-01 and NLW-GT-01 wells. The identification of more fractures in VAL-01 compared to NLW-GT-01 can be explained by the difference in basin location. VAL-01 was located in the centre of the basin where distal playa environments produced fine-grained material alternating with coarser sands. The more proximal NLW-GT-01 was dominated by fluvial sands. The lithological variability produces Young’s modulus variability that seems to be driving increased fracture density rather than that the absolute value of the Young’s modulus. These fluctuations could be the result of concentrated compressional stress, which is supported by the existence of stylolites, indicative of high compressional stresses in the same intervals. The identification of controls on fracture distribution in the targeted stratigraphic interval can be used to optimize the planning of future geothermal doublets and to de-risk upcoming operations.

The southern Chotts basin (SCB), Central Tunisia, has shown hydrocarbon potential since the end of the 1980s. This basin records a complex structural history which appears decoupled at the Hercynian or Variscan unconformity. The Paleozoic series is deformed by short to medium wavelength folds (kilometres-multi kilometres scale) and by steep normal faults. The Mesozoic series is largely less deformed. The evolution of the basin through time is still a matter of debate as the preserved Paleozoic series is fragmented (e.g. affected by erosions). In this paper, we proposed a reconstruction of the vertical movements affecting the basin and an evaluation of their magnitude. Using basin modelling techniques, we provided new insights on the possible thermal evolution of the basin that might be used in the future exploration phases. This study was completed by structural restorations allowing the reconstruction of the paleogeography of the basin at the time of deposition of principal reservoir formations.

The positive impact that natural fractures can have on geothermal heat production from low-permeability reservoirs has become increasingly recognised and proven by subsurface case studies. In this study, we assess the potential impact of natural fractures on heat extraction from the tight Lower Buntsandstein Subgroup targeted by the recently drilled NLW-GT-01 well (West Netherlands Basin (WNB)). We integrate: (1) reservoir property characterisation using petrophysical analysis and geostatistical inversion, (2) image-log and core interpretation, (3) large-scale seismic fault extraction and characterisation, (4) Discrete Fracture Network (DFN) modelling and permeability upscaling, and (5) fluid-flow and temperature modelling. First, the results of the petrophysical analysis and geostatistical inversion indicate that the Volpriehausen has almost no intrinsic porosity or permeability in the rock volume surrounding the NLW-GT-01 well. The Detfurth and Hardegsen sandstones show better reservoir properties. Second, the image-log interpretation shows predominately NW-SE-orientated fractures, which are hydraulically conductive and show log-normal and negative-power-law behaviour for their length and aperture, respectively. Third, the faults extracted from the seismic data have four different orientations: NW-SE, N-S, NE-SW and E-W, with faults in proximity to the NLW-GT-01 having a similar strike to the observed fractures. Fourth, inspection of the reservoir-scale 2D DFNs, upscaled permeability models and fluid-flow/temperature simulations indicates that these potentially open natural fractures significantly enhance the effective permeability and heat production of the normally tight reservoir volume. However, our modelling results also show that when the natural fractures are closed, production values are negligible. Furthermore, because active well tests were not performed prior to the abandonment of the Triassic formations targeted by the NLW-GT-01, no conclusive data exist on whether the observed natural fractures are connected and hydraulically conductive under subsurface conditions. Therefore, based on the presented findings and remaining uncertainties, we propose that measures which can test the potential of fracture-enhanced permeability under subsurface conditions should become standard procedure in projects targeting deep and potentially fractured geothermal reservoirs.

Understanding fractures and fracture networks is essential for the investigation and use of subsurface reservoirs. The aim is to predict the fractures and the fracture network when there is no direct access to subsurface images available. This article presents a universal workflow to numerically compute a discrete fracture network by combining the 1D scanline survey method, processed with the newly written SkaPy script, together with the multiple point statistic method (MPS). This workflow is applied to a potential geothermal site in Mexico called Acoculco. We use Las Minas outcrops and quarries as surface analogues for the Acoculco reservoir, as Las Minas and Acoculco are both formed by the influence of a plutonic intrusion into the Jurassic-Cretaceous carbonate sequence of the Sierra Madre Oriental in the Trans-Mexican volcanic belt (TMVB). The intrusion is associated with contact metamorphism and metasomatic phenomena, providing the basis for the mining activities at Las Minas. The results obtained using this workflow demonstrate the feasibility of the approach, which presents a solution combining the efficiency of data processing and an interpretation-driven approach to build realistic discrete fracture networks. This workflow can be used in the process of estimating the permeability of a fracture controlled reservoir, with using only scanline surveys data as input. This is essential in the process of evaluating the feasibility to develop an enhanced geothermal system.

The use of the subsurface and the exploitation of subsurface resources require prior knowledge of fluid flow through fracture networks. For nuclear waste disposal, for the enhancement of hydrocarbon recovery from a field, or the development of an enhanced geothermal system (EGS), it is fundamental to constrain the fractures and the fracture network. This study is part of the GEMex project, an international collaboration of two consortia, one from Europe and one from Mexico. The research is based on exploration, characterization and assessment of two geothermal systems located in the Trans-Mexican volcanic belt, Los Humeros and Acoculco. In Acoculco, two wells reached very high temperatures, but did not find any fluids. For that reason, the Acoculco Caldera is foreseen as an EGS development site, hoping to connect existing wells to a productive zone. This implies that the fluid flow through the geothermal reservoir would be mainly fracture dominated. This study investigates the dependency of fracture permeability, constrained by fracture lengths and apertures, with stress field conditions. Simulations are computed in 2D, using COMSOL Multiphysics^® Finite Elements Method Software, populated with mechanical data obtained in the rock physics laboratory and with dense discrete fracture networks generated from 1D scanline surveys measured in Las Minas analogue outcrops for Acoculco reservoir. The method offers a prediction for multiple scenarios of the reservoir flow characteristics which could be a major improvement in the development of the EGS technology.

Natural fracture network characteristics can be establishes from high-resolution outcrop images acquired from drone and photogrammetry. Such images might also be good analogues of subsurface naturally fractured reservoirs and can be used to make predictions of the fracture geometry and efficiency at depth. However, even when supplementing fractured reservoir models with outcrop data, gaps will remain in the model and fracture network extrapolation methods are required. In this paper we used fracture networks interpreted from two outcrops from the Apodi area, Brazil, to present a revised and innovative method of fracture network geometry prediction using the multiple-point statistics (MPS) method.

The MPS method presented in this article uses a series of small synthetic training images (TIs) representing the geological variability of fracture parameters observed locally in the field. The TIs contain the statistical characteristics of the network (i.e. orientation, spacing, length/height and topology) and allow for the representation of a complex arrangement of fracture networks. These images are flexible, as they can be simply sketched by the user.

We proposed to simultaneously use a set of training images in specific elementary zones of the Apodi outcrops in order to best replicate the non-stationarity of the reference network. A sensitivity analysis was conducted to emphasise the influence of the conditioning data, the simulation parameters and the training images used. Fracture density computations were performed on selected realisations and compared to the reference outcrop fracture interpretation to qualitatively evaluate the accuracy of our simulations. The method proposed here is adaptable in terms of training images and probability maps to ensure that the geological complexity in the simulation process is accounted for. It can be used on any type of rock containing natural fractures in any kind of tectonic context. This workflow can also be applied to the subsurface to predict the fracture arrangement and fluid flow efficiency in water, geothermal or hydrocarbon fractured reservoirs.

Natural fractures conduct fluids in subsurface reservoirs. Quick and realistic predictions of the fracture network organization and its fluid flow efficiency from limited amount of data is critical to optimize resources productivity. We recently developed a method based on multiple point statistics (MPS) technique to produce geologically-constrained fracture network simulations. The method allows to account for the intrinsic non-stationarity of these networks by considering a multivariate input data instead of averaged distribution of fracture parameters. In addition, the method considers probability maps reflecting the influence of fracture drivers in the network variability. Consequently, the simulated fracture networks derived from the innovative MPS approach are geologically better constrained than in classical discrete fracture network modelling approaches. This paper proposes to apply this method in subsurface conditions where available data are sparsely distributed. We developed a workflow where data are gathered from wellbore and from additional sources (outcrops). These data are used to extrapolate a network around the borehole as training images and themselves are extrapolated at the reservoir scale following a geological probability map.This work also presents innovations on the way how training images and probability maps that may integrate more geology constrain than relying almost entirely on available data.

It sounds counter-intuitive to consider contraction features such as stylolites as potential conduits for flow. However, this idea has grown since 1980, with geoscientists finding many examples principally in carbonate reservoirs where stylolites can be considered as fluid-efficient features. Among others, these features can be reactivated stylolites, can generate positive porosity and permeability anomalies, can drive corrosive fluids or can remain open in an overpressured system. Conversely, stylolites can also be closed forever. These impermeable stylolites can generate permeability anisotropy that may impact fluid movements. Stylolites require particular attention to evaluate whether they act as drains or as barriers to flow (compartmentalisation). We review some of the key studies of the past thirty years with a special attention to the most recent ones. We end-up considering their mechanical origin, their nucleation and growth, their past and present impact on reservoir properties and performances as key factors influencing the flow efficiency differentiation of these features. This short review presents the latest theories and observations about stylolites with respect to the key factors aforementioned. The authors support herein that a distinction should be made between processes occurring in the past and the present-day impact the stylolite had on reservoir properties.

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.