A multi-core numerical framework for characterizing flow in oil reservoirs

Leonardi, Christopher R., Holmes, David W., Williams, John R., and Tilke, Peter G. (2011) A multi-core numerical framework for characterizing flow in oil reservoirs. In: Proceedings of the 2011 Spring Simulation Multiconference (6) pp. 166-174. From: SpringSim '11 Spring Simulation Multiconference, 3 - 7 April 2011, Boston, MA, USA.

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This paper presents a numerical framework that enables scalable, parallel execution of engineering simulations on multi-core, shared memory architectures. Distribution of the simulations is done by selective hash-tabling of the model domain which spatially decomposes it into a number of orthogonal computational tasks. These tasks, the size of which is critical to optimal cache blocking and consequently performance, are then distributed for execution to multiple threads using the previously presented task management algorithm, H-Dispatch. Two numerical methods, smoothed particle hydrodynamics (SPH) and the lattice Boltzmann method (LBM), are discussed in the present work, although the framework is general enough to be used with any explicit time integration scheme. The implementation of both SPH and the LBM within the parallel framework is outlined, and the performance of each is presented in terms of speed-up and efficiency. On the 24-core server used in this research, near linear scalability was achieved for both numerical methods with utilization efficiencies up to 95%. To close, the framework is employed to simulate fluid flow in a porous rock specimen, which is of broad geophysical significance, particularly in enhanced oil recovery.

Item ID: 16805
Item Type: Conference Item (Research - E1)
ISBN: 978-1-930638-56-3
Keywords: parallel computation, multi-core, smoothed particle hydrodynamics, lattice Boltzmann method, enhanced oil recovery
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Funders: Schlumberger-Doll Research
Date Deposited: 20 Apr 2012 05:19
FoR Codes: 01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational Mathematics > 010301 Numerical Analysis @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0805 Distributed Computing > 080599 Distributed Computing not elsewhere classified @ 50%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 50%
97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 50%
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