文献类型：期刊文献

英文题名：A predictive model correlating permeability to two-dimensional fracture network parameters

作者：Liu, Richeng[1];Zhu, Tantan[1];Jiang, Yujing[2,3,4];Li, Bo[5];Yu, Liyuan[1];Du, Yan[6];Wang, Yingchao[1]

机构：[1]China Univ Min & Technol, State Key Lab Geomech & Deep Underground Engn, Xuzhou 221116, Jiangsu, Peoples R China;[2]Nagasaki Univ, Sch Engn, Nagasaki 8528521, Japan;[3]Shandong Univ Sci & Technol, State Key Lab Min Disaster Prevent & Control Shan, Qingdao 266590, Shandong, Peoples R China;[4]Shandong Univ Sci & Technol, Minist Sci & Technol, Qingdao 266590, Shandong, Peoples R China;[5]Shaoxing Univ, Rock Mech & Geohazards Ctr, Shaoxing 312000, Peoples R China;[6]Univ Sci & Technol Beijing, Sch Civil & Environm Engn, Beijing 100083, Peoples R China

年份：2019

卷号：78

期号：3

起止页码：1589

外文期刊名：BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT

收录：SCI-EXPANDED(收录号：WOS:000463751500021)、、EI(收录号：20180504692960)、Scopus(收录号：2-s2.0-85041133367)、WOS

基金：The financial supports from the National Key Basic Research and Development Program of China, China (No. 2017YFC0603001), National Natural Science Foundation of China, China (Nos. 51734009, 51709260, 51579239), and Natural Science Foundation of Jiangsu Province, China (No. BK20170276) are gratefully acknowledged.

语种：英文

外文关键词：discrete fracture network; predictive model; Monte Carlo technique; connectivity; rock mass permeability

外文摘要：This study presents a predictive model of permeability with correlation to parameters of two-dimensional fracture networks, including mass density of fractures (d(m)), average number of intersections per meter at the inlet and outlet boundaries (d(in)), and connectivity (C-r). The fracture networks were constructed by considering the influence of fracture number density, fracture length, orientation, and variance of fracture length. A total of 86 discrete fracture network (DFN) models were established using the Monte Carlo technique, and the relationships between permeability and d(m), d(in), and C-r were analyzed. By fitting these calculated results, a multi-variable regression function was proposed for predicting permeability. The results show that the number density of fractures plays a more significant role in permeability than fracture length. Fracture orientation can change the connectivity of fracture networks robustly, and thereafter influence the permeability. Although variance of fracture length can affect the pattern of cumulative frequency - fracture length curves, the variance has negligible influence on the permeability of fracture networks. A necessary condition to form connected flow paths from inlet boundary to outlet boundary is: d(m)>0.27m/m(2), d(in)>0.19 /m, and C-r>1.20. The proposed regression function can predict permeability with a correlation coefficient larger than 0.87, and its validity is verified by comparisons with results reported in the literature. Finally, the potential future works that can facilitate the predictive models of permeability are pointed out as open questions.