Accurate Computation of Fracture Density Variations: A New Approach Tested on Fracture Corridors
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Accurate Computation of Fracture Density Variations: A New Approach Tested on Fracture Corridors Sophie Viseur1 · Juliette Lamarche1 · Clément Akriche2 · Sébastien Chatelée2 · Metzger Mombo Mouketo2 · Bertrand Gauthier3
Received: 29 November 2018 / Accepted: 12 October 2020 © International Association for Mathematical Geosciences 2020
Abstract Fracture density is an important parameter for characterizing fractured reservoirs. Stochastic object-based simulation algorithms that generate fracture networks commonly rely on a fracture density to populate the reservoir zones with individual fracture surfaces. Reservoirs, including fracture corridors, represent particular challenges in petroleum reservoir studies. Indeed, it is difficult to identify fracture corridor zones objectively and precisely along one-dimensional well data, which are characterized by high fracture densities compared to diffuse fractures. To estimate fracture density, a common practice is to graphically depict only fracture corridors on fracture cumulative intensity curves. In this paper, an approach is proposed to formalize this technique using hypothesis testing. This method precisely compartmentalizes the well data into several zones having specific fracture densities. The method consists of the following steps: (i) dividing the diagram into zones depending on a priori drastic changes in density, (ii) computing the local accurate fracture density for each zone and (iii) clustering the zones characterized by similar densities statistically. The key point is to couple regression and hypothesis testing. The regression aims at computing local average fracture density and the hypothesis testing aims at clustering zones for which the densities are statistically the most similar. The proposed approach is dedicated to one-dimensional fracture surveys, such as well data and outcrop scanlines. First, a synthetic case study is presented to prove the ability to highlight changes in fracture
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Sophie Viseur [email protected]
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CNRS, IRD, INRAE, Coll France, CEREGE, Aix Marseille University, 13545 Aix-en-Provence, France
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Aix Marseille University, 3 Place Victor Hugo, 13331 Marseille cedex 03, France
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Total EP, Tour Coupole, 2 Place Jean Millier, La Défense 6, 92078 Paris-La Défense Cedex, France
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Math Geosci
density. Second, the procedure is applied on a scanline dataset collected in a quarry (Calvisson, SE France) to show the usefulness of characterizing fracture corridors. Keywords Fracture density · Hypothesis testing · Regression · Classification · Fracture corridor
1 Introduction Fractures have major impacts on fluid flow in most types of tight reservoirs (Antonellini and Aydin 1994; Gauthier et al. 2000; Hansford and Fishe 2009; Agosta et al. 2010). Many geostatistical approaches have been proposed to model three-dimensional fracture networks (Chilès 1988; Bonneau et al. 2013). These geostatistical methods generally require inputs such as fracture sizes, orientations and densities. Fracture abundance, termed as Pi j, has been defined
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