GPU Accelerated Stochastic Inversion of Deep Water Seismic Data
Seismic inversion algorithms have been playing a key role in the characterization of oil and gas reservoirs, where a high accuracy is often required to support the decision about the optimal well locations. Since these algorithms usually rely on computer simulations that generate, process and store significant amounts of data, their usage is often limited by their long execution times. In fact, the acceleration of these algorithms allows not only a faster execution, but also the development of larger and more accurate models of the subsurface. This paper proposes a novel parallelization approach of a state of art Stochastic Seismic Amplitude versus Offset Inversion algorithm, by using heterogeneous computing platforms based on a unified OpenCL programming framework. To take full advantage of the computational power made available by systems composed by multiple (and possibly different) accelerators, a spatial division of the simulation space is performed, enabling the parallel simulation of multiple regions of the geological model. This allows achieving a performance speed-up of 22.8× using two distinct GPUs without compromising the accuracy of the obtained models.
KeywordsStochastic Inversion of Seismic Data Heterogeneous computing Graphics Processing Unit (GPU) OpenCL
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