Fractures have a significant impact on rock mass mechanical and hydraulic properties, which is a concern for rock engineering applications like excavation or repository design, support design, slope stability and caving in mines. To address this issue, a sound description of the fracturing pattern is required. DFN models are statistical models which define the density of fractures having given geometrical properties (size and orientation) and which include an intrinsic variability term. One of the main challenging task is to combine all available data. Data remain sparse and scarce and are acquired at different scales and from different support shapes and dimension (1D, 2D). We present a 3D modelling approach combining data from borehole logs, outcrop trace maps and tunnel walls mapping. It is applied to the Äspö site in Sweden, for which a large database is available, containing tens of thousands of records. Using stereological rules and assumptions about the underlying DFN scaling model, we are able to integrate all data to define the fracturing properties from the borehole scale (ten centimeters) to the repository scale (several kilometers). An advanced DFN modeling framework is applied, accounting for fractures mechanical interactions. This model has proved to be almost universal in crystalline rocks and reproduces, with very few parameters, the scaling properties of fractures. We show that this modelling framework better reproduces observations at all available scales and yields DFN, which structure and associated properties have a better consistency with natural cases than for simple DFN approaches.
Studying fractured systems is a requirement for many industrial applications including nuclear waste deep repositories, geothermal energy exploitation in crystalline hard rocks and oil and shale gas extraction. In these fields, fractures are key factors for rock masses flow and mechanical properties. Fracture networks are complex systems arising from the physics of fracture development and from complex interactions between fractures. Because of this intrinsic complexity, fracture systems present the classical characteristics of complex systems with power-law scaling relationships . This is now widely recognized from geological studies [2–4].
The major difficulty for defining Discrete Fracture Network (DFN) models is the limited amount and the nature of available data. Despite constant technical improvements, high-resolution measurements of fracture patterns are mostly limited to borehole and surface mapping, thus raising both under-sampling and stereological issues. The fracture system is defined at best from statistical distributions, which are the basic ingredients for interpolating local measurements at the site scale. The precise knowledge of these distributions, including scaling, is a critical issue for site modeling.