HatchFrac: A fast open-source DFN modeling software. (October 2022)

• Record Type: Journal Article
• Authors: Zhu, Weiwei; Khirevich, Siarhei; Patzek, Tadeusz W.
• Is Part Of: Computers and geotechnics. Volume 150(2022)
• Publication Date: 2022-10
• Subjects: Discrete fracture networks -- Fracture intersections -- Fracture clusters -- Machine learning -- Newman–Ziff algorithm; Engineering geology -- Data processing -- Periodicals; Soil mechanics -- Data processing -- Periodicals; Rock mechanics -- Data processing -- Periodicals; 624.1510285
• DOI: 10.1016/j.compgeo.2022.104917
• Languages: English
• ISSNs: 0266-352X

Abstract

Abstract: 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. Highlights: A comprehensive C++ software package for stochastic modeling of 2/3-D fracture networks is presented. Detailed procedures and algorithms of DFN modeling are covered. Three applications of HatchFrac software are introduced to show the versatility.