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

Zheng Li, Shuhong Wu, Chen Song Zhang, Jinchao Xu, Chunsheng Feng, Xiaozhe Hu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)93-102
Number of pages10
JournalComputing and Visualization in Science
Issue number2-3
StatePublished - Jan 1 2017
Externally publishedYes


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