Use of a Massively Parallel SIMD Computer for Reservoir Simulation

Abstract

Summary Massively parallel single-instruction, multiple-data (SIMD) computers have shown much promise in solving numerically intensive problems ranging from molecular modeling to computational fluid dynamics. Much of the research on the use of parallel computing for reservoir simulation, however, has been parallel computing for reservoir simulation, however, has been limited to coarse-grain, shared memory computers or medium-grain hypercubes. We report here on research performed on a massively parallel SIMD computer with 65,536 processors. This work addresses issues in machine architecture, programming environment, and formulation of a reservoir simulator programming environment, and formulation of a reservoir simulator on SIMD computers. Towards this end, a three phase, three dimensional, IMPES compositional simulator was developed in Fortran 8X. Problems associated with reservoir simulation on SIMD computers such as an appropriate data structure for the SIMD architecture, the treatment of multiple fluid phases, data communication, and the matrix solution are addressed in this paper. In addition, table lookup poses a unique problem for SIMD computers because storage of large tables on each processor is impractical, as is the use of the front-end computer to perform this function. The problem is overcome through a novel use of local memory access. We found that massively parallel computers can be used to run very large reservoir models and that the cost of data transfer between processors need not be prohibitive. Reservoir models with two hydrocarbon components and up to 2,097,152 grid blocks were successfully run using this simulator. Computational speeds on the order of one giga FLOPS were achieved for the generation of the Jacobian mix and the matrix solution using 65,536 processors. Introduction The current trend for improving supercomputer performance is to increase the intrinsic speed of the individual processors and/or to add more processors. As clock speed approaches the maximum physical limit for semiconductors, the addition of more processors is physical limit for semiconductors, the addition of more processors is the more promising direction. Parallel computers, in particular those with distributed memory, have generated much interest for numerically intensive applications. These parallel computers hold the promise of being more cost effective than supercomputers with shared memory, and have the potential to solve much larger problems.

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