Statistical Fracture Domain methodology for DFN modeling applied to site characterization

International audienceFractures in rock masses strongly influence the underground mechanical and hydrogeological behavior. Understanding the relation between fracturing properties and rock properties is essential and still is a research area. In parallel, it is as much important to be able to characterize the fracturing properties into DFN models whose mean estimates and estimate accuracy are well defined. Fracturing properties indeed combine multi-scale range of sizes together with sharp variations of densities and orientation organization, which prevents from any simple characterization. In this paper we focus on the notion of variability: how it can be assessed, what is its importance and how it is transmitted in the modeling, from local scale to largest site scale. The DFN characterization is only based on depth core logging data and the DFN models relate to densities and orientation distributions. We describe a method recently developed, called SFD (Statistical Fracture Domain), which is used to first appraise the uncertainty of density estimates due to local variability, next to quantify and compute differences (statistical distance) between any number of dataset density estimates and finally to group datasets into classes of compatible statistical properties. The method is applied to some data from the SKB Forsmark site