Abstract
Large-scale reservoir simulation demands significant computational time so improving its computational efficiency becomes crucial. Graphics Processing Unit (GPU), a high-profile parallel processor with hundreds of microprocessors, offers great potential in parallel reservoir simulation because of its efficient power utilization and high computational efficiency. In addition, its cost is relatively low, making large-scale parallel reservoir simulation possible for most of desktop users. In this presentation several GPU-based parallel linear solvers and preconditioners will be discussed. They include the GMRES, BiCGSTAB and ORTHOMIN solvers and the incomplete LU (ILU) factorization, domain decomposition and algebraic multigrid preconditioners. These solvers and preconditioners have been coupled with an in-house black-oil simulator to speedup reservoir simulation. In the numerical experiments performed, the SPE 10 problem, a 3D heterogeneous benchmark model with over one million grid blocks, is selected to test the speedup of the resulting black-oil simulator. On the state-of-the-art CPU and GPU platforms, the new GPU implementation can achieve a speedup of over eight times in solving linear systems arising from this SPE 10 problem compared with the CPU implementation.
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Acknowledgements
The support of Department of Chemical and Petroleum Engineering, University of Calgary and Reservoir Simulation Group is gratefully acknowledged. The research is partly supported by NSERC/AIEES/Foundation CMG and AITF Chairs.
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Chen, Z., Liu, H., Yu, S., Hsieh, B., Shao, L. (2014). GPU-Based Parallel Reservoir Simulators. In: Erhel, J., Gander, M., Halpern, L., Pichot, G., Sassi, T., Widlund, O. (eds) Domain Decomposition Methods in Science and Engineering XXI. Lecture Notes in Computational Science and Engineering, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-319-05789-7_16
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