Abstract
Fractures and their connectivity are essential for fluid flow in low permeability formations. Geological outcrops can only provide two-dimensional (2D) information, but subsurface fractures are three-dimensional (3D). The percolation status of 3D fracture networks and their 2D cross-section maps are rarely investigated simultaneously. In this work, we construct 3D fracture networks with their geometries characterized by different stochastic distributions. Cross-section maps are taken to mimic real outcrops, and clusters are labeled to check the percolation status of 3D fracture networks and their 2D cross-section maps. The properties, reflecting the connectivity of two essential phases, are summarized and analyzed. We find that clustering effects impact local intersections significantly but have negligible impacts on fracture intensities of 3D fracture networks. The number of intersections per fracture is not a proper percolation parameter for complex 2D and 3D fracture networks. Real fracture networks in the subsurface should be geometrically well-connected and over-percolative if their outcrop maps have a spanning cluster formed. Empirical limits are provided to predict the fracture intensity and connectivity of subsurface 3D fracture networks based on their outcrop maps. If a spanning cluster is formed in the outcrop map, the corresponding 3D fracture networks should be over-percolative, and its fracture intensity can be larger than 3.5 times the intensity at percolation. However, if no spanning cluster is formed in the outcrop map, but the fracture intensity is larger than 0.43 times the intensity at percolation, the corresponding 3D fracture networks can form a spanning cluster and show good connectivity.
•3D fracture networks and their 2D cross-section maps are investigated by SDFN.•The percolation status of 3D/2D fracture networks are systematically analyzed.•Impacts of fracture geometries on the percolation status are analyzed.•Natural fractures can be over-percolative if its outcrop forms a spanning cluster.