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) and acceptance–rejection methods are applied to generate random variables from the stochastic distributions commonly used in discrete fracture network (DFN) modeling. The multilayer perceptron (MLP) machine learning approach, combined with the inverse CDF method, generates random variables following any sampling distribution. We extend the Newman–Ziff algorithm to determine clusters in the fracture networks and make the code faster. When combined with the block method, the coding efficiency is further enhanced. The software generates the T-type fracture intersections in the network by simulating a fracture growth process, which can be used in applications involving fracture growth or incorporating geomechanics. Three applications of HATCHFRAC are introduced to demonstrate the versatility of our software: percolation analysis, fracture intensity analysis, and flow and connectivity analysis.
Bibliographical noteKAUST Repository Item: Exported on 2022-09-14
Acknowledgements: This project was supported by the baseline research funding from KAUST, Saudi Arabia to Prof. Tadeusz W. Patzek.