This paper introduces a comprehensive C++ software package, HatchFrac, for stochastic modelling of fracture networks in two and three dimensions. Two main methods, the inverse CDF method and acceptance-rejection method, are applied to generate random variables from the stochastic distributions commonly used in discrete fracture network (DFN) modelling. The multilayer per-ceptron (MLP) machine learning approach, combined with the inverse CDF method, is implemented to generate random variables following any sampling distribution. To make the code faster, we extend the Newman-Ziff to determine clusters in the fracture networks. When combined with the block method, the Ziff algorithm improves the coding efficiency significantly. The software generates the T-type fracture intersections in the network, which can be used in applications involving fracture growth or incorporating geomechanics. We introduce three applications of HatchFrac that demonstrate the versatility of our software: percolation analysis, fracture intensity analysis, and flow and connectivity analysis.

Fractures play an essential role in formations with low permeability; however, fracture sealing significantly reduces the permeability of fractures. The mechanism of how fracture sealing impacts the macro-scale fluid flow is rarely investigated. Here, we simulate sealing in two- and three-dimensional orthogonal fracture networks and investigate the impact of sealing on the percolation of these fracture networks. We find that a small amount of sealing can prevent the formation of spanning clusters, which suggests that global connectivity is rarely realized. Without significant stress perturbations, most fractures are partially sealed and non-critically stressed, and they usually do not contribute much to the fluid flow. However, under a significant stress perturbation, such as hydraulic fracturing, the well-connected and critically oriented fractures become critically stressed and slide because of the increased pore pressure. Partially sealed and non-critically stressed fractures can also contribute to the fluid flow by enlarging the stimulated reservoir volume (SRV). We estimate the stimulated reservoir volume in two dimensions by dividing the target distance (LSRV) into two parts. One is the distance limiting generation of hydraulic fractures (ΔLh), and the other is the limiting distance of making natural fractures slide (ΔLs). A rough estimation yields an elongated shape of the SRV, which is consistent with observations from microseismicity maps.