Abstracts for the 1 st International Conference on Discrete Fracture Network
Engineering (DFNE), Vancouver, October 19-22, 2014
Site DFN modeling: assessment of 3D statistical distributions at
different scales, and of statistically identical fracture domains. SKB
sites examples.
Caroline Darcel (1), Philippe Davy (2), Romain Le Goc (1), Isabelle Olofsson (3)
(1)
ITASCA consultants, Chemin des Mouilles, Ecully, France
Geosciences Rennes, UMR 6118, CNRS, Université de Rennes 1, Campus de Beaulieu, 35042 Rennes, France
(3)
Svensk Kärnbränslehantering AB, Box 250, 101 24 Stockholm
(2)
We address site DFN modeling issues through an approach based on a scale analysis of fracture
densities, including variability, and on a classification process that is aimed to define the minimum set
of independent Statistical Fracture Models necessary to describe data. The method is applied to the
SKB sites in Sweden.
Modeling the spatial variations of DFN properties is a major issue of site characterization. Spatial
variations are likely related to in-situ geological factors (stress history, depth, lithology …) but also to
the fracturing processes themselves. The latter are known to produce complex fracture patterns that
result from the interplay between fractures over multiple length scales. This entails an intrinsic
variability of fracture densities that should be included into the elementary DFN description. The
characterization process is all the more difficult as sampling is limited to partial observations (core,
outcrop, tunnel surface data) of the 3D structure of DFNs (and 2D structure of fractures) and is only
performed through a very small fraction of the total volume of interest. With these conditions, the
DFN characterization is a statistical model, which includes intrinsic variability, and rely on a few
(modeling) assumptions.
For each elementary dataset, we derive the first and second moment of the DFN statistical distribution
from a scaling analysis. The fracturing density is calculated in 3D by means of stereological rules.
This step requires some assumptions about the underlying DFN scaling model, especially when
combining data acquired at different scales and from different support shapes. The next step consists
in clustering DFN distributions that are statistically identical. This classification process determines
the minimum number of Statistical Fracture Models (density distribution functions) which describes
the complete database (and relative site).
For the application to SKB sites, the selected database encompasses up to hundred thousand records of
fractures from various supports (cored boreholes, tunnel and surface outcrops). We discuss the
classification process and the resulting fracturing description. The final classification is mainly
controlled by the fracturing intensity and by the depth dependence of horizontal fracture density.