Abstract
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.
Highlights
The sensitivity analysis presented in this paper is a way to compare the multiple-point statistics (MPS) simulations with the reference outcrop
To reinforce the evaluation of the proposed method, we quantified the values of fracture intensity in the reference outcrop and in three selected AP3 MPS simulations and the optimised simulation (OPT1; Fig. 10)
This is in agreement with the results of the sensitivity analysis showing that SIM 26 and OPT 1 best represent the number of fractures present in the reference outcrop
Summary
1.1 The importance of the prediction of fracture network geometryFractures 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-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. type, orientation, size and topology) that are determined from specific stress and strain conditions. 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). Outcrops are essential to characterise fracture network attributes that cannot be sampled in the subsurface, such as length or spatial connectivity
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