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Impact of a stochastic sequential initiation of fractures on the spatial correlations and connectivity of discrete fracture networks
Author(s) -
Bonneau François,
Caumon Guillaume,
Renard Philippe
Publication year - 2016
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1002/2015jb012451
Subject(s) - point process , fracture (geology) , percolation (cognitive psychology) , spatial correlation , statistical physics , dimension (graph theory) , contrast (vision) , computer science , correlation , stochastic process , scale (ratio) , mathematics , statistics , geology , artificial intelligence , physics , geometry , geotechnical engineering , quantum mechanics , neuroscience , pure mathematics , biology
Abstract Stochastic discrete fracture networks (DFNs) are classically simulated using stochastic point processes which neglect mechanical interactions between fractures and yield a low spatial correlation in a network. We propose a sequential parent‐daughter Poisson point process that organizes fracture objects according to mechanical interactions while honoring statistical characterization data. The hierarchical organization of the resulting DFNs has been investigated in 3‐D by computing their correlation dimension. Sensitivity analysis on the input simulation parameters shows that various degrees of spatial correlation emerge from this process. A large number of realizations have been performed in order to statistically validate the method. The connectivity of these correlated fracture networks has been investigated at several scales and compared to those described in the literature. Our study quantitatively confirms that spatial correlations can affect the percolation threshold and the connectivity at a particular scale.