Fractures and their connectivity are essential for fluid flow in low perme-ability formations. Abundant outcrops can only provide two-dimensional (2D) information, but subsurface fractures are three-dimensional (3D). The percola-tion 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. Then, we take cross-section maps to mimic real outcrops and label clusters 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. Fracture intensities are scale-dependent and usually decrease with increasing scales. The real fracture networks in the subsurface should be geometrically well-connected and pervasive if their outcrop maps are well connected. In particular, the fracture intensity of the real fracture network can be several times (at least 3.6 times) larger than the intensity at percolation. However, if outcrop maps are not well-connected, but their intensities are large enough (at least 0.43 times as large as the intensity at percolation), corresponding 3D fracture networks can also form a spanning cluster and show good connectivity with a high possibility.

Stimulated reservoir volume (SRV), the high-permeable fracture network created by hydraulic fracturing, is essential for fluid production from low-permeable reservoirs. However, the configuration of SRV and its impacting factors are largely unknown. In this work, we adopt the stochastic discrete fracture network method to mimic natural fractures in subsurface formations and conduct a global sensitivity analysis with the Sobol method. The sensitivity of different fracture properties, including geometrical properties (fracture lengths, orientations and center positions), mechanical properties (fracture roughness and fracture strength), fracture sealing properties (probabilities of open fractures and segment lengths) and the fracture intensity, are investigated in two and three-dimensional fracture networks. JRC-JCS model is adopted to identify critically stressed fractures. We find that critically stressed fractures compose the backbone of SRV, while partially open fractures can significantly enlarge the size of SRV by connecting more critically orientated fractures. The fracture roughness is the most influential factor for the total length (area) of critically stressed fractures. For the relative increase of SRV (RI) in 2D/3D fracture networks, the probability of open fractures is the most significant factor. The fracture lengths and center positions are essential factors for RI in 2D fracture networks but insignificant in 3D fracture networks. This work provides a realistic scenario of the subsurface structure and systematically investigates the influential factors of SRV, which is useful for estimating the size of SRV and predicting shale gas reservoirs’ production in an accurate and physically meaningful way.