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

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• 作者：Zheng Li ; Shuhong Wu ; Chen-Song Zhang ; Jinchao Xu&#8230
• 关键词：Reservoir simulation ; Algebraic multigrid ; Krylov subspace method ; Multi ; stage preconditioning
• 刊名：Computing and Visualization in Science
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：18
• 期：2-3
• 页码：93-102
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Computational Mathematics and Numerical Analysis; Computer Applications in Chemistry; Algorithms; Visualization; Numerical Analysis; Calculus of Variations and Optimal Control; Optimization;
• 出版者：Springer Berlin Heidelberg
• ISSN：1433-0369
• 卷排序：18

文摘

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.