An improved multistage preconditioner on GPUs for compositional reservoir simulation
An improved multistage preconditioner on GPUs for compositional reservoir simulation
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DOI：
10.1007/s42514-023-00136-0
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发表时间：
2022-08
影响因子：
0.9
通讯作者：
Li Zhao;Shizhe Li;Chensong Zhang;Chunsheng Feng;S. Shu
中科院分区：
文献类型：
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作者：
Li Zhao;Shizhe Li;Chensong Zhang;Chunsheng Feng;S. Shu
成分模型通常用于描述石油工业中的多组分多相多孔介质流。全隐式方法稳定性强，时间步长约束弱，是主流商业油藏模拟器中常用的方法。在本文中，我们开发了一种用于完全隐式合成流模拟的高效多级预处理器。该方法采用自适应设置阶段来提高 GPU 上的并行效率。此外，在压力部分的代数多重网格方法中应用了基于邻接矩阵的多色Gauss-Seidel算法。数值结果表明，所提出的算法实现了良好的并行加速，同时产生与相应的顺序版本相同的收敛行为。
The compositional model is often used to describe multicomponent multiphase porous media flows in the petroleum industry. The fully implicit method with strong stability and weak constraints on time-step sizes is commonly used in mainstream commercial reservoir simulators. In this paper, we develop an efficient multistage preconditioner for the fully implicit compositional flow simulation. The method employs an adaptive setup phase to improve the parallel efficiency on GPUs. Furthermore, a multicolor Gauss–Seidel algorithm based on the adjacency matrix is applied in the algebraic multigrid methods for the pressure part. Numerical results demonstrate that the proposed algorithm achieves good parallel speedup while yielding the same convergence behavior as the corresponding sequential version.