Mixed precision parallel preconditioner and linear solver for compositional reservoir simulation

作者：Khaz' ali Ali Reza; Rasaei Mohammad Reza^*; Moghadasi Jamshid

来源：Computational Geosciences, 2014, 18(5): 729-746.

DOI：10.1007/s10596-014-9421-3

• Compositional modeling
• GPGPU
• Iterative linear solver
• Many-core processor
• Minimal residual method
• Partial sorting
• Simultaneous solution

Reservoir simulation role in value creation and strategic management decisions cannot be over emphasized. Simulation of complex challenging reservoirs with millions of grid blocks especially in compositional mode is very time-consuming even with fast modern computers. On the other hand, high price of cluster supercomputers prevents them for being commonly used for fast simulation of such reservoirs. In recent years, the development of many-core processors like cell processors, DSPs, and graphical processing units (GPUs) has provided a very cost-effective hardware platform for fast computational operations. However, programming for such processors is much more difficult than conventional CPUs, and new parallel algorithm design and special parallel implementation methods are needed. Using the computational power of CPUs, GPUs, and/or any other processing unit, Open Computing Language (OpenCL) provides a framework for programming for heterogeneous platforms. In this paper, OpenCL is used to employ the computational power of a GPU to build a preconditioner and solve the linear system arising from compositional formulation of multiphase flow in porous media. The proposed parallel preconditioner is proved to be quite effective, even in heterogeneous porous media. Using data-parallel modules on GPU, the preconditioner/solver runtime reduced at least 1 order of magnitude compared to their serial implementation on CPU.

• 出版日期2014-10
• 单位中国石油大学（北京）