Industry pressures, including large full field simulation, modeling enhanced recovery from mature assets, and the rapid development of unconventional assets, are creating a huge demand for large, fast, high-fidelity reservoir simulation. Similar needs in seismic imaging have been addressed by running on GPUs, but the application of GPUs to something as complex as reservoir simulation presents challenges. We report on a practical multi-GPU approach to reservoir simulation that provides fast turn-around times on large models of upwards of tens of millions of cells on a single workstation. All major steps of the fully implicit CPR-AMG preconditioned black-oil simulation including property evaluation and Jacobian construction are directly evaluated on the GPUs. In this process, the abundant fine grain parallelism of the GPU cores are fully exposed and utilized. Because the simulations are memory-bandwidth bound, our data structures and algorithms are optimized to efficiently utilize the limited GPU memory and increase data locality for coalesced access. Weak-scaling tests of black-oil simulations using tiled SPE-10 models validate our approach for multiple GPUs. We conclude that GPUs can deliver performance to industry demands, and this approach will see further benefits with new generations of many-core hardware.


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