The stochastic discrete fracture network (SDFN) model is a practical approach to model complex fracture systems in the subsurface. However, it is impossible to validate the correctness and quality of an SDFN model because the comprehensive subsurface structure is never known. We utilize a pixel-based fracture detection algorithm to digitize 80 published outcrop maps of different scales at different locations. The key fracture properties, including fracture lengths, orientations, intensities, topological structures, clusters and flow are then analyzed. Our findings provide significant justifications for statistical distributions used in SDFN modellings. In addition, the shortcomings of current SDFN models are discussed. We find that fracture lengths follow multiple (instead of single) power-law distributions with varying exponents. Large fractures tend to have large exponents, possibly because of a small coalescence probability. Most small-scale natural fracture networks have scattered orientations, corresponding to a small κ value (κ<3) in a von Mises–Fisher distribution. Large fracture systems collected in this research usually have more concentrated orientations with large κ values. Fracture intensities are spatially clustered at all scales. A fractal spatial density distribution, which introduces clustered fracture positions, can better capture the spatial clustering than a uniform distribution. Natural fracture networks usually have a significant proportion of T-type nodes, which is unavailable in conventional SDFN models. Thus a rule-based algorithm to mimic the fracture growth and form T-type nodes is necessary. Most outcrop maps show good topological connectivity. However, sealing patterns and stress impact must be considered to evaluate the hydraulic connectivity of fracture networks.

T-type intersections are commonly observed in natural fracture networks. Their impacts on the connectivity and flow results in complex fracture networks are rarely investigated. In this work, we implement the discrete fracture network method to construct complex fracture networks, denoted as original fracture networks. By implementing the rule-based fracture growth algorithm, we generate the corresponding kinematic fracture networks with a substantial proportion of T-type intersections. The connectivity and flow results of both the single-phase and two-phase flow simulations in these two types of fracture networks are systematically investigated. The results show that kinematic fracture networks tend to connect more fractures with fewer intersections and yield better connectivity than the original ones. Most kinematic fracture networks have larger permeability in the single-phase flow simulation and higher cumulative gas production in the two-phase flow simulation than original fracture networks under the same boundary conditions. The proportions of permeability and production enhancement are 68\% and 77\%, respectively. Flow results, like the permeability and production, have strong positive correlations with the connectivity of the fracture networks, but they are nonequivalent and strongly impacted by the number of inlets and outlets.