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Abstract

Summary

Percolation theory is widely used to analyze connectivity of fracture networks. The quantities commonly used to characterize fracture networks are the total excluded area, total self-determined area, and number of intersections per fracture. These three quantities are percolation parameters for the constant-length fracture networks, but no one has investigated them in complex fracture networks. We investigate variability of these three quantities in three types of fracture networks, in which fracture lengths follow a power-law distribution, fracture orientations follow a uniform distribution, and fracture center positions follow either a uniform distribution (type 1 and 2) or a fractal spatial density distribution (type 3). We show that in type 1 and type 2 fracture networks, these three quantities are percolation parameters only when the power-law exponent is larger than 3.5. In type 3 fracture networks, none of the three quantities are percolation parameters. We also investigate 16 outcrop fracture maps and find that these maps are closest to type 3 fracture networks. The outcrop fractures cluster and have lengths that follow power law distribution with the exponent ranging from 2 to 3.

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/content/papers/10.3997/2214-4609.201801134
2018-06-11
2024-04-19

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References

  1. Balberg, I., Anderson, C.H., Alexander, S. and Wagner, N.
    [1984] Excluded volume and its realtion to the onset of percolation. Phsical Review B, 30(7), 3933–3943.
     [Google Scholar]
  2. Barton, C.C.
    [1995] Fractal Analysis of Scaling and Spatial Clustering of Fractures. In: Fractals in the Earth Sciences, 141–178.
     [Google Scholar]
  3. Berkowitz, B.
    [1995] Analysis of fracture network connectivity using percolation theory. Mathematical Geology, 27(4), 467–483.
     [Google Scholar]
  4. Bour, O. and Davy, P.
    [1997] Connectivity of random fault networks following a power law fault length distribution. Water Resources Research, 33(7), 1567–1583.
     [Google Scholar]
  5. Gross, M.R.
    [1993] The origin and spacing of cross joints: examples from the Monterey Formation, Santa Barbara Coastline, California. Journal of Structural Geology, 15(6), 737–751.
     [Google Scholar]
  6. Ledesert, B., Dubois, J., Velde, B., Meunier, A., Genter, A. and Badri, A.
    [1993] Geometrical and fractal analysis of a three-dimensional hydrothermal vein network in a fractured granite. Journal of Volcanology and Geothermal Research, 56(3), 267–280.
     [Google Scholar]
  7. Masihi, M., King, P. and Nurafza, P.
    [2007] Fast Estimation of Connectivity in Fractured Reservoirs Using Percolation Theory. SPE Journal, 12(2), 13–16.
     [Google Scholar]
  8. Meakin, P.
    [1991] Invasion percolation on substrates with correlated disorder. Physica A: Statistical Mechanics and its Applications, 173(3), 305–324.
     [Google Scholar]
  9. Stauffer, D. and Aharony, a.
    [1994] Introduction to Percolation Theory.
     [Google Scholar]
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Percolation Properties of Stochastic Fracture Networks in 2D and Outcrop Fracture Maps
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201801134
/content/papers/10.3997/2214-4609.201801134
/content/papers/10.3997/2214-4609.201801134
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