Connectivity Field: a Measure for Characterising Fracture Networks
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Connectivity Field: a Measure for Characterising Fracture Networks Younes Fadakar Alghalandis · Peter A. Dowd · Chaoshui Xu
Received: 13 March 2013 / Accepted: 9 January 2014 © International Association for Mathematical Geosciences 2014
Abstract Analysis of the connectivity of a fracture network is an important component of the design, assessment and development of fracture-based reservoirs in geothermal, petroleum and groundwater resource applications. It is a useful means of characterising the flow pathways and the mechanical behaviours of reservoirs. An appropriate practical measure is required for connectivity characterisation because of the extreme complexity of fracture networks. In this paper, we propose the connectivity field (CF), as a useful measure to evaluate the spatial connectivity characteristics of fractures in a fracture network. The CF can be applied on both a particular realisation of a fracture network model (for deterministic evaluation) and on stochastic fracture network models using stochastic modelling and Monte Carlo simulations (for probabilistic evaluation with uncertainties). Two extensions are also proposed: the generalised connectivity field, a measure that is independent of support size, and the probabilistic connectivity field. Potential applications of the CF and its extensions are in determining the optimal location of an injection or production well so as to maximise reservoir performance and in determining potential flow pathways in fracture networks. The average CF map shows strong correlations with the X f and P21 measures. The relationships between the CF measures, the fracture intersection density and the fracture network connectivity index are also investigated. Keywords Connectivity field · Connectivity index · Discrete fracture network · Fractured reservoir · Intersection density
Y. Fadakar Alghalandis (B) · P. A. Dowd · C. Xu School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia e-mail: [email protected]; [email protected]
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Math Geosci
1 Introduction Fracture network modelling (FNM) is an important component of the design and development of natural energy and resource systems including geothermal and petroleum reservoirs and aquifers (Freeze 1975; CFCFF 1996; Cacas et al. 2001; Nelson 2001; Jing 2003; Hanano 2004; Kvartsberg 2010; Singhal and Gupta 2010; Fadakar-A et al. 2013a; Seifollahi et al. 2013). In general, the productivity of the reservoir depends critically on connections between injection and production wells and the areal extent of the fracture network in the reservoir. This is particularly true in hot dry rock geothermal energy systems, as connections between injection and production wells provide pathways for the geothermal flow. It is, therefore, vitally important to understand the connectivity of the fracture network in such reservoirs and the methods proposed in this paper will contribute to achieving this understanding. Stochastic FNM provides a means of incorporating uncer
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