HatchFrac: A fast open-source DFN modeling software

This paper introduces a comprehensive C++ software package, HatchFrac, for stochastic modeling of fracture networks in two and three dimensions. The inverse cumulative distribution function (CDF) acceptance–rejection methods are applied to generate random variables from the distributions commonly used discrete network (DFN) modeling. multilayer perceptron (MLP) machine learning approach, combined with CDF method, generates following any sampling distribution. We extend Newman–Ziff algorithm determine clusters make code faster. When block coding efficiency is further enhanced. T-type intersections by simulating growth process, which can be applications involving or incorporating geomechanics. Three HatchFrac introduced demonstrate versatility our software: percolation analysis, intensity flow connectivity analysis.