1887

Abstract

Summary

Demands for higher fidelity and predictive capability from reservoir simulation have resulted in the development of reservoir simulation models in the hundreds of millions of cells. When combined with the need to quickly assess many realizations of such models, the cluster size and power requirements can become impractical. GPUs provide an extremely dense and efficient computational platform that can help reduce the required hardware footprint and power envelope. We discuss our attempt to efficiently scale reservoir simulation to the GPU cluster using a combination of CUDA and MPI. We describe several potential bottlenecks on performance and our strategies to them. We give examples on synthetic and real-field models, assessing both performance and accuracy. Finally, we discuss the surrounding workflow challenges which must be met to make best use of an extremely high-performance simulator.

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/content/papers/10.3997/2214-4609.201414022
2015-09-13
2024-04-20
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References

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