论文标题
平行的多阶段预处理，具有适用于黑油模型的自适应设置
Parallel Multi-Stage Preconditioners with Adaptive Setup for the Black Oil Model
论文作者
论文摘要
黑色油模型被广泛用于描述石油行业中多相的多孔培养基流。完全隐式的方法具有强大的稳定性和时间步长的较弱约束；因此，通常用于当前主流商业储层模拟器。在本文中，开发了具有自适应“设置阶段”的CPR型预处理，以提高石油储层模拟的平行效率。此外，我们提出了一种基于强连接系数矩阵的代数多机方法的多色高斯 - 西德尔（GS）算法。数值实验表明，所提出的预处理可以改善OpenMP和CUDA工具的并行性能。此外，所提出的算法与相应的单线程算法相同的平行加速和收敛行为。特别是，对于三相基准问题，OpenMP版本的并行加速超过6.5，有16个线程，CUDA版本的达到9.5以上。
The black oil model is widely used to describe multiphase porous media flow in the petroleum industry. The fully implicit method features strong stability and weak constraints on time step-sizes; hence, commonly used in the current mainstream commercial reservoir simulators. In this paper, a CPR-type preconditioner with an adaptive "setup phase" is developed to improve parallel efficiency of petroleum reservoir simulation. Furthermore, we propose a multi-color Gauss-Seidel (GS) algorithm for algebraic multigrid method based on the coefficient matrix of strong connections. Numerical experiments show that the proposed preconditioner can improve the parallel performance for both OpenMP and CUDA implements. Moreover, the proposed algorithm yields good parallel speedup as well as same convergence behavior as the corresponding single-threaded algorithm. In particular, for a three-phase benchmark problem, the parallel speedup of the OpenMP version is over 6.5 with 16 threads and the CUDA version reaches more than 9.5.
