Stochastic sequential simulation of genetic-like discrete fracture networks

Francois Bonneau and Guillaume Caumon and Philippe Renard and Judith Sausse. ( 2012 )
in: Proc. 32nd Gocad Meeting, Nancy

Abstract

This paper presents a stochastic sequential approach to simulate Discrete Fracture Networks (dfns). dfns aim at reproducing both the network statistics (such as fracture extent, dip, strike and density) and the hierarchical organization of fractures. Natural fracture networks appear and grow gradually when the stress intensity reaches the fracture toughness. Older fractures, because of their influences on both stress field and rock coherence, impact later growth and seeding of younger fractures. The proposed approach aims at reproducing natural fracture network by combining a seeding process conditioned by existing shadow and damage zone of former fractures with a growth process setting locally a consistent fracture geometry. Simulations have been run on synthetic examples. As compared to classical dfns simulations, the proposed method yields dfns which honor input statistics but also show a better consistency with field observations and mechanical principles.

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BibTeX Reference

@INPROCEEDINGS{BonneauGM2012,
    author = { Bonneau, Francois and Caumon, Guillaume and Renard, Philippe and Sausse, Judith },
     title = { Stochastic sequential simulation of genetic-like discrete fracture networks },
 booktitle = { Proc. 32nd Gocad Meeting, Nancy },
      year = { 2012 },
  abstract = { This paper presents a stochastic sequential approach to simulate Discrete Fracture Networks (dfns). dfns aim at reproducing both the network statistics (such as fracture extent, dip, strike and density) and the hierarchical organization of fractures. Natural fracture networks appear and grow gradually when the stress intensity reaches the fracture toughness. Older fractures, because of their influences on both stress field and rock coherence, impact later growth and seeding of younger fractures. The proposed approach aims at reproducing natural fracture network by combining
a seeding process conditioned by existing shadow and damage zone of former fractures with
a growth process setting locally a consistent fracture geometry.
Simulations have been run on synthetic examples. As compared to classical dfns simulations, the proposed method yields dfns which honor input statistics but also show a better consistency with field observations and mechanical principles. }
}