Dimensional threshold for fracture linkage and hooking

Lamarche, Juliette; Chabani, Arezki; Gauthier, B.

doi:10.1016/j.jsg.2017.11.016

Cited by 31 publications

(7 citation statements)

References 29 publications

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“…Fractures are widespread in nature and, depending on their density and their aperture, might have a strong impact on fluid flow and fluid aquifers (Berkowitz, 2002;Rzonca, 2008), in addition to potentially affecting geothermal (Montanari et al, 2017;Wang et al, 2016) and hydrocarbon reservoirs (Agar and Geiger, 2015;Lamarche et al, 2017;Solano et al, 2010) They are typically organised as networks rang-538 P.-O. Bruna et al: A new methodology to train fracture network simulation ing from the nanometre to the multi-kilometre scale (Zhang et al, 2016), and they present systematic geometrical characteristics (i.e.…”

Section: The Importance Of the Prediction Of Fracture Network Geometrymentioning

confidence: 99%

“…These conditions have been used to derive concepts of fracture arrangements in various tectonic contexts and introduced the notion of geological fracture-drivers (fault, fold, burial and facies). Based on these drivers, it is possible to some extent to predict reservoir heterogeneity and to define potential permeability pathways within the rock mass (Lamarche et al, 2017;Laubach et al, 2018). Despite the existence of these concepts, a range of parameters including fracture abutment relationships and height/length distributions cannot be adequately sampled along a 1-D borehole and are mainly invisible on seismic images.…”

Section: The Importance Of the Prediction Of Fracture Network Geometrymentioning

confidence: 99%

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A new methodology to train fracture network simulation using multiple-point statistics

Bruna¹,

Straubhaar²,

Prabhakaran³

et al. 2019

Solid Earth

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Abstract. 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.

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Section: The Importance Of the Prediction Of Fracture Network Geometrymentioning

confidence: 99%

Section: The Importance Of the Prediction Of Fracture Network Geometrymentioning

confidence: 99%

A new methodology to train fracture network simulation using multiple-point statistics

Bruna¹,

Straubhaar²,

Prabhakaran³

et al. 2019

Solid Earth

View full text Add to dashboard Cite

show abstract

“…Fractures are ubiquitous in Nature and are known to impact fluid flow and fluid storage in water (Berkowitz, 2002;Rzonca, 2008), heat (Montanari et al, 2017;Wang et al, 2016) and hydrocarbon reservoirs (Agar and Geiger, 2015;Lamarche et al, 2017). They are typically Solid Earth Discuss., https://doi.org/10.5194/se-2018-106 Manuscript under review for journal Solid Earth Discussion started: 11 October 2018 c Author(s) 2018.…”

Section: I1 the Importance Of The Prediction Of Fracture Network Geomentioning

confidence: 99%

“…These conditions are typically well known in folded (Suppe, 1983(Suppe, , 1985Tavani et al, 2015;Watkins et al, 2017), faulted (Faulkner et al, 2010;Matonti et al, 2012;Micarelli et al, 2006) and burial-related contexts (Bertotti et al, 2017;Bisdom, 2016) and have been used to derive concepts of fracture arrangements. These concepts are currently used to predict reservoir heterogeneity and to define potential permeability pathways within the rock mass (Lamarche et al, 2017;Laubach et al, 2018). Despite the existence of these concepts, fracture networks generally present an intrinsic complexity (e.g.…”

Section: I1 the Importance Of The Prediction Of Fracture Network Geomentioning

confidence: 99%

A new methodology to train fracture network simulation using Multiple Point Statistic

Bruna¹,

Straubhaar²,

Prabhakaran³

et al. 2018

Preprint

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Abstract. 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.

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“…The low rock matrix permeability and porosity allow the fluid flow within fracture networks [9][10][11]. The understanding of the spatial arrangement of the fracture network constitutes the main issue in fractured reservoirs [1,3,7,12,13].…”

Section: Introductionmentioning

confidence: 99%

Fracture Spacing Variability and the Distribution of Fracture Patterns in Granitic Geothermal Reservoir: A Case Study in the Noble Hills Range (Death Valley, CA, USA)

et al. 2021

Self Cite

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Scanlines constitute a robust method to better understand in 3D the fracture network variability in naturally fractured geothermal reservoirs. This study aims to characterize the spacing variability and the distribution of fracture patterns in a fracture granitic reservoir, and the impact of the major faults on fracture distribution and fluid circulation. The analogue target named the Noble Hills (NH) range is located in Death Valley (DV, USA). It is considered as an analogue of the geothermal reservoir presently exploited in the Upper Rhine Graben (Soultz-sous-Forêts, eastern of France). The methodology undertaken is based on the analyze of 10 scanlines located in the central part of the NH from fieldwork and virtual (photogrammetric models) data. Our main results reveal: (1) NE/SW, E/W, and NW/SE fracture sets are the most recorded orientations along the virtual scanlines; (2) spacing distribution within NH shows that the clustering depends on fracture orientation; and (3) a strong clustering of the fracture system was highlighted in the highly deformed zones and close to the Southern Death Valley fault zone (SDVFZ) and thrust faults. Furthermore, the fracture patterns were controlled by the structural heritage. Two major components should be considered in reservoir modeling: the deformation gradient and the proximity to the regional major faults.

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Dimensional threshold for fracture linkage and hooking

Cited by 31 publications

References 29 publications

A new methodology to train fracture network simulation using multiple-point statistics

A new methodology to train fracture network simulation using multiple-point statistics

A new methodology to train fracture network simulation using Multiple Point Statistic

Fracture Spacing Variability and the Distribution of Fracture Patterns in Granitic Geothermal Reservoir: A Case Study in the Noble Hills Range (Death Valley, CA, USA)

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