Reduced Fracture Finite Element Model Analysis of an Efficient Two-Scale Hybrid Embedded Fracture Model
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ArticleAuthors
Amir, Sahar
Chen, Huangxin
Sun, Shuyu

KAUST Department
Computational Transport Phenomena LabEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2017-06-09Online Publication Date
2017-06-09Print Publication Date
2017Permanent link to this record
http://hdl.handle.net/10754/625064
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A Hybrid Embedded Fracture (HEF) model was developed to reduce various computational costs while maintaining physical accuracy (Amir and Sun, 2016). HEF splits the computations into fine scale and coarse scale. Fine scale solves analytically for the matrix-fracture flux exchange parameter. Coarse scale solves for the properties of the entire system. In literature, fractures were assumed to be either vertical or horizontal for simplification (Warren and Root, 1963). Matrix-fracture flux exchange parameter was given few equations built on that assumption (Kazemi, 1968; Lemonnier and Bourbiaux, 2010). However, such simplified cases do not apply directly for actual random fracture shapes, directions, orientations …etc. This paper shows that the HEF fine scale analytic solution (Amir and Sun, 2016) generates the flux exchange parameter found in literature for vertical and horizontal fracture cases. For other fracture cases, the flux exchange parameter changes according to the angle, slop, direction, … etc. This conclusion rises from the analysis of both: the Discrete Fracture Network (DFN) and the HEF schemes. The behavior of both schemes is analyzed with exactly similar fracture conditions and the results are shown and discussed. Then, a generalization is illustrated for any slightly compressible single-phase fluid within fractured porous media and its results are discussed.Citation
Amir SZ, Chen H, Sun S (2017) Reduced Fracture Finite Element Model Analysis of an Efficient Two-Scale Hybrid Embedded Fracture Model. Procedia Computer Science 108: 1873–1882. Available: http://dx.doi.org/10.1016/j.procs.2017.05.052.Sponsors
King Abdullah University of Science and Technology (KAUST) funding supported the research reported in this publication.Publisher
Elsevier BVJournal
Procedia Computer ScienceAdditional Links
http://www.sciencedirect.com/science/article/pii/S1877050917305835ae974a485f413a2113503eed53cd6c53
10.1016/j.procs.2017.05.052