KAUST DepartmentAli I. Al-Naimi Petroleum Engineering Research Center (ANPERC), King Abdullah University of Science and Technology, Jeddah, Mecca, KSA
Energy Resources & Petroleum Engineering
Physical Science and Engineering (PSE) Division
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
Energy Resources and Petroleum Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/673950
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AbstractThis 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.
CitationZhu, W., Khirevich, S., & Patzek, T. W. (2022). HatchFrac: A fast open-source DFN modeling software. Computers and Geotechnics, 150, 104917. https://doi.org/10.1016/j.compgeo.2022.104917
SponsorsThis project was supported by the baseline research funding from KAUST, Saudi Arabia to Prof. Tadeusz W. Patzek.
JournalComputers and Geotechnics
Except where otherwise noted, this item's license is described as © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).