Hierarchical multiscale modeling for flows in fractured media using generalized multiscale finite element method

被引:43
|
作者
Efendiev Y. [1 ,2 ]
Lee S. [3 ]
Li G. [4 ]
Yao J. [5 ]
Zhang N. [5 ]
机构
[1] Department of Mathematics and Institute for Scientific Computation (ISC), Texas A&M University, College Station, TX
[2] SRI Center for Numerical Porous Media (NumPor), King Abdullah University of Science and Technology (KAUST), Thuwal
[3] Chevron ETC, Houston, 77002, TX
[4] Department of Mathematics, Texas A&M University, College Station, 77843-3368, TX
[5] School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266555, Shandong
基金
中国国家自然科学基金;
关键词
Discrete fracture model; Fractured media; GMsFEM; Multiscale finite element;
D O I
10.1007/s13137-015-0075-7
中图分类号
学科分类号
摘要
In this paper, we develop a multiscale finite element method for solving flows in fractured media. Our approach is based on generalized multiscale finite element method (GMsFEM), where we represent the fracture effects on a coarse grid via multiscale basis functions. These multiscale basis functions are constructed in the offline stage via local spectral problems following GMsFEM. To represent the fractures on the fine grid, we consider two approaches (1) discrete fracture model (DFM) (2) embedded fracture model (EFM) and their combination. In DFM, the fractures are resolved via the fine grid, while in EFM the fracture and the fine grid block interaction is represented as a source term. In the proposed multiscale method, additional multiscale basis functions are used to represent the long fractures, while short-size fractures are collectively represented by a single basis functions. The procedure is automatically done via local spectral problems. In this regard, our approach shares common concepts with several approaches proposed in the literature as we discuss. We would like to emphasize that our goal is not to compare DFM with EFM, but rather to develop GMsFEM framework which uses these (DFM or EFM) fine-grid discretization techniques. Numerical results are presented, where we demonstrate how one can adaptively add basis functions in the regions of interest based on error indicators. We also discuss the use of randomized snapshots (Calo et al. Randomized oversampling for generalized multiscale finite element methods, 2014), which reduces the offline computational cost. © 2015, Springer-Verlag Berlin Heidelberg.
引用
收藏
页码:141 / 162
页数:21
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