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AAPG Bulletin, V.
Evaluation of sampling methods for fracture network characterization using outcrops
1Karlsruhe Institute of Technology, Institute for Applied Geosciences, Kaiserstrasse 12, 76131 Karlsruhe, Germany; present address: Geotechnical Institute, TU Bergakademie Freiberg, Gustav-Zeuner-Strase 1, 09596 Freiberg, Germany; [email protected]
2University of Tubingen, Department of Geosciences, Wilhelmstrasse 56, 72074 Tubingen, Germany; [email protected]
3University of Tubingen, Department of Geosciences, Wilhelmstrasse 56, 72074 Tubingen, Germany; [email protected]
4Karlsruhe Institute of Technology, Institute for Applied Geosciences, Kaiserstrasse 12, 76131 Karlsruhe, Germany; [email protected]
Outcrops provide valuable information for the characterization of fracture networks. Sampling methods such as scanline sampling, window sampling, and circular scanline and window methods are available to measure fracture network characteristics in outcrops and from well cores. These methods vary in their application, the parameters they provide and, therefore, have advantages and limitations. We provide a critical review on the application of these sampling methods and apply them to evaluate two typical natural examples: (1) a large-scale satellite image from the Oman Mountains, Oman (120,000 m2 [1,291,669 ft2]), and (2) a small-scale outcrop at Craghouse Park, United Kingdom (19 m2 [205 ft2]). The differences in the results emphasize the importance to (1) systematically investigate the required minimum number of measurements for each sampling method and (2) quantify the influence of censored fractures on the estimation of fracture network parameters. Hence, a program was developed to analyze 1300 sampling areas from 9 artificial fracture networks with power-law length distributions. For the given settings, the lowest minimum number of measurements to adequately capture the statistical properties of fracture networks was found to be approximately 110 for the window sampling method, followed by the scanline sampling method with approximately 225. These numbers may serve as a guideline for the analyses of fracture populations with similar distributions. Furthermore, the window sampling method proved to be the method that is least sensitive to censoring bias. Reevaluating our natural examples with the window sampling method showed that the existing percentage of censored fractures significantly influences the accuracy of inferred fracture network parameters.
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