Numerical studies of a class of linear solvers for fine-scale petroleum reservoir simulation

Springer Science and Business Media LLC - Tập 18 - Trang 93-102 - 2016
Zheng Li1, Shuhong Wu2, Chen-Song Zhang3, Jinchao Xu4, Chunsheng Feng5,6, Xiaozhe Hu7
1Kunming University of Science and Technology, Kunming, China
2PetroChina Research Institute of Petroleum Exploration and Development, Beijing, China
3LSEC and NCMIS, Academy of Mathematics and Systems Science, Beijing, China
4Department of Mathematics, Penn State University, University Park, USA
5School of Mathematics and Computational Science, Xiangtan University, Hunan, China
6Guangdong Provincial Engineering Technology Research Center for Data Science, Guangzhou, China
7Department of Mathematics, Tufts University, Medford, USA

Tóm tắt

Numerical simulation based on fine-scale reservoir models helps petroleum engineers in understanding fluid flow in porous media and achieving higher recovery ratio. Fine-scale models give rise to large-scale linear systems, and thus require effective solvers for solving these linear systems to finish simulation in reasonable turn-around time. In this paper, we study convergence, robustness, and efficiency of a class of multi-stage preconditioners accelerated by Krylov subspace methods for solving Jacobian systems from a fully implicit discretization. We compare components of these preconditioners, including decoupling and sub-problem solvers, for fine-scale reservoir simulation. Several benchmark and real-world problems, including a ten-million-cell reservoir problem, were simulated on a desktop computer. Numerical tests show that the combination of the alternating block factorization method and multi-stage subspace correction preconditioner gives a robust and memory-efficient solver for fine-scale reservoir simulation.

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