Nonlinear Preconditioning Strategies for Two-Phase Flows in Porous Media Discretized by a Fully Implicit Discontinuous Galerkin Method

Li Luo, Xiao-Chuan Cai, David E. Keyes

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

We consider numerical simulation of two-phase flows in porous media using implicit methods. Because of the complex features involving heterogeneous permeability and nonlinear capillary effects, the nonlinear algebraic systems arising from the discretization are very difficult to solve. The traditional Newton method suffers from slow convergence in the form of a long stagnation or sometimes does not converge at all. In this paper, we develop nonlinear preconditioning strategies for the system of two-phase flows discretized by a fully implicit discontinuous Galerkin method. The preconditioners identify and approximately eliminate the local high nonlinearities that cause the Newton method to take small updates. Specifically, we propose two elimination strategies: one is based on exploring the unbalanced nonlinearities of the pressure and the saturation fields, and the other is based on identifying certain elements of the finite element space that have much higher nonlinearities than the rest of the elements. We compare the performance and robustness of the proposed algorithms with an existing single-field elimination approach and the classical inexact Newton method with respect to some physical and numerical parameters. Experiments on three-dimensional porous media applications show that the proposed algorithms are superior to other methods in terms of robustness and parallel efficiency.
Original languageEnglish (US)
Pages (from-to)S317-S344
Number of pages1
JournalSIAM Journal on Scientific Computing
DOIs
StatePublished - Jun 9 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-06-11
Acknowledged KAUST grant number(s): 11701547, N-KUST620/15.
Acknowledgements: The work of the first author was partially supported by NSFC grant 11701547 and NSFC-RGC joint research grant N-KUST620/15.

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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