可扩展的大型油藏数值模拟并行计算平台开发
Development of A Scalable Platform for Large-scale Reservoir Simulations on Parallel computers
摘要：
本文介绍了我们的工作，设计了一个并行平台的大规模油藏模拟。详细介绍了网格求解器、线性求解器和数据结构等组件，为油藏数值模拟和其他并行应用提供了指导。该平台的主要目标是支持在分布式存储并行系统上实现各种并行油藏模拟器，其中MPI（消息传递接口）用于计算节点之间的通信。它提供了结构化的网格，由于其简单性和细胞为中心的数据适用于每个细胞。该平台具有分布式矩阵和矢量模块以及地图模块。矩阵和向量模是并行线性系统的基础。该映射连接网格和线性系统模块，定义了网格和线性系统之间的各种映射。实现了常用的Krylov子空间线性求解器，包括重新启动的GMRES方法和BiCGSTAB方法。它也有一个接口，并行代数多重网格求解器，BoomerAMG从HYPRE。并行通用预处理和特殊的预处理油藏模拟也被开发。设计了网格、单元、数据、线性求解器和预处理器等各种数据结构，并给出了一些关键的缺省参数。数值实验表明，该平台具有良好的可扩展性，可以使用数千个CPU内核模拟具有数亿个网格单元的巨型油藏模型。
英文摘要：
This paper presents our work on designing a parallel platform for large-scale reservoir simulations. Detailed components, such as grid and linear solver, and data structures are introduced, which can serve as a guide to parallel reservoir simulations and other parallel applications. The main objective of platform is to support implementation of various parallel reservoir simulators on distributed-memory parallel systems, where MPI (Message Passing Interface) is employed for communications among computation nodes. It provides structured grid due to its simplicity and cell-centered data is applied for each cell. The platform has a distributed matrix and vector module and a map module. The matrix and vector module is the base of our parallel linear systems. The map connects grid and linear system modules, which defines various mappings between grid and linear systems. Commonly-used Krylov subspace linear solvers are implemented, including the restarted GMRES method and the BiCGSTAB method. It also has an interface to a parallel algebraic multigrid solver, BoomerAMG from HYPRE. Parallel general-purpose preconditioners and special preconditioners for reservoir simulations are also developed. Various data structures are designed, such as grid, cell, data, linear solver and preconditioner, and some key default parameters are presented in this paper. The numerical experiments show that our platform has excellent scalability and it can simulate giant reservoir models with hundreds of millions of grid cells using thousands of CPU cores.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
