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The tectonic evolution of rift basins can strongly influence the petrophysical properties of its sedimentary infills at various scales and, therefore, influence the characteristics of present resources. Understanding the tectonic evolution of a rift basin is therefore decisive, as it can have consequences for the parameters that influence the planning of geothermal doublets. In this framework, this study gives a detailed interpretation of the syn- and post-rift episodes in the West Netherlands Basin, since Jurassic times. Despite multiple studies on the geotectonic setting of the West Netherlands Basin, a detailed understanding of its syn- and post-rift phases in the context of geothermal exploration is still fundamentally lacking. With a renewed interpretation of a seismic 3D cube, covering a large portion of the onshore section of the basin, we identified two important Jurassic rifting episodes and one Cretaceous inversion event. Rifting caused compartmentalisation of the main producing geothermal target in the area; the Late Jurassic Nieuwerkerk Formation. Yet, the central portions of the half-grabens show good potential for geothermal exploration. Subsequent inversion could have caused local breaching of the aquifer, forming a potential risk. Therefore, only non to moderately inverted areas should be considered for geothermal exploration. This study provides a better understanding of the multi-phase rifting history in the West Netherlands Basin, providing important constraints on the reservoir-seal integrity and with that, the amount of heat that can be safely produced from a geothermal reservoir rock. Aiming to contribute to the energy transition, this study provides an integrated picture of the West Netherlands Basin and shows how a basin's geological history can affect its geothermal resources. [ABSTRACT FROM AUTHOR]

We investigate the spatial variation of 2D fracture networks digitized from the well-known Lilstock limestone pavements, Bristol Channel, UK. By treating fracture networks as spatial graphs, we utilize a novel approach combining graph similarity measures and hierarchical clustering to identify spatial clusters within fracture networks and quantify spatial variation. We use four graph similarity measures: fingerprint distance, D-measure, NetLSD, and portrait divergence to compare fracture graphs. The technique takes into account both topological relationship and geometry of the networks and is applied to three large fractured regions consisting of nearly 300,000 fractures spread over 14,200 sq.m. The results indicates presence of spatial clusters within fracture networks with that vary gradually over distances of tens of metres. One region is not influenced by faulting but still displays variation in background fracturing. Variation in fracture development in the other two regions are interpreted to be primarily influenced by proximity to faults that gradually gives way to background fracturing. Comparative analysis of the graph similarity-derived clusters with fracture persistence measures indicate that there is a general correspondence between patterns; however, additional variations are highlighted that is not obvious from fracture intensity and density plots. The proposed method provides a quantitative way to identify spatial variations in fracture networks which can be used to guide stochastic and geostatistical approaches to fracture network modelling. [ABSTRACT FROM AUTHOR]

The 100,000 m² wave-cut pavement in the Bristol Channel near Lilstock, UK, is a world-class outcrop, perfectly exposing a very large fracture network in several thin limestone layers. We present an analysis based on manual interpretation of fracture generations in selected domains and compare this with automated fracture tracing. Our dataset of high-resolution aerial photographs of the complete outcrop was acquired by unmanned aerial vehicle, using a survey altitude optimized to resolve all fractures. We map fractures and identify fracture generations based on abutting and overprinting criteria and present the fracture networks of five selected representative domains. Each domain is also mapped automatically using ridge detection based on the complex shearlet transform method. The automatic fracture detection technique provides results close to the manually mapped fracture networks, however, with a bias towards closely spaced Y over X nodes. The assignment of fractures into generations cannot be done automatically yet. Generations one and two are long fractures that traverse our domains. Generation three is only present in the southwestern domains. Generation four follows an ENE-WSW striking trend, is sub-orthogonal to generations one and two and abuts on them and generation 3 if present. Generations five is the youngest fracture set with diffuse orientation, creating polygonal patterns by abutting on all older fracture generations. Our mapping results show that the northeastern domains only contain four fracture generations, thus the five generations of the outcrop identified in the southwestern domains are either not all present in each of the five domains or vary locally in their geometry, preventing the interpreter to link the fractures to their respective generation over several spatially separate mapping domains. The domains have different P21 fracture intensities which is lowest in the NE with 7.31 m/m² and highest in the SW with 10.01 m/m², coinciding with different fracture orientations, and distributions of abutting relationships. Each domain has slightly different fracture network characteristics. The network analysis shows that connectivity increases, depending on the evolving fracture generations. [ABSTRACT FROM AUTHOR]

Natural fractures have a strong impact on flow and storage properties of reservoirs. Their distribution in the subsurface is largely unknown mainly due to their sub-seismic scale and to the scarcity of available data sampling them (borehole). Outcrop can be considered as analogues where natural fracture characteristics can be extracted. However, acquiring fracture data on outcrops may produce a large amount of information that needs to be processed and efficiently interpreted to capture the key parameters defining fracture network geometry. Outcrops thus become a natural laboratory where the interpreted fracture network can be tested mechanically (fracture aperture, distribution of strain/stress) and dynamically (fluid flow simulations (Bisdom et al., 2017). The goal of this paper is to propose the multiple point statistics (MPS) method as a new tool to quickly predict the geometry of a fracture network in both surface and subsurface conditions. This sequential simulation method is based on the creation of small and synthetic training images representing fracture distribution parameters observe in the field. These training images represent the complexity of the geological object or processes to be simulated and can be simply designed by the user. In this paper we chose to use multiple training images and a probability map to represent the fracture network geometry and its potential variability in a non-stationary manner. The method was tested on a fracture pavement (2D flat surface) acquired using a drone in the Apodi area in Brazil. Fractures were traced manually on images of the outcrop and constitute the reference on which the fracture network simulations will be based. A sensitivity analysis emphasizing the influence of the conditioning data, the simulation parameters and the used training images was conducted on the obtained simulations. Stress-induced fracture aperture calculations were performed on the best realisations and on the original outcrop fracture interpretation to qualitatively evaluate the accuracy of our simulations. The method proposed here is innovative and adaptable. It can be used on any type of rocks containing natural fractures in any kind of tectonic context. This workflow can also be applied to the subsurface to predict the fracture arrangement and its fluid flow efficiency in water, heat or hydrocarbon reservoirs. [ABSTRACT FROM AUTHOR]