GPU Accelerated Stochastic Inversion of Deep Water Seismic Data

  • Tomás Ferreirinha
  • Rúben Nunes
  • Amílcar Soares
  • Frederico Pratas
  • Pedro Tomás
  • Nuno Roma
Conference paper

DOI: 10.1007/978-3-319-14325-5_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8805)
Cite this paper as:
Ferreirinha T., Nunes R., Soares A., Pratas F., Tomás P., Roma N. (2014) GPU Accelerated Stochastic Inversion of Deep Water Seismic Data. In: Lopes L. et al. (eds) Euro-Par 2014: Parallel Processing Workshops. Euro-Par 2014. Lecture Notes in Computer Science, vol 8805. Springer, Cham

Abstract

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.

Keywords

Stochastic Inversion of Seismic Data Heterogeneous computing Graphics Processing Unit (GPU) OpenCL 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tomás Ferreirinha
    • 1
  • Rúben Nunes
    • 2
  • Amílcar Soares
    • 2
  • Frederico Pratas
    • 1
  • Pedro Tomás
    • 1
  • Nuno Roma
    • 1
  1. 1.INESC-ID / ISTUniversidade de LisboaPortugal
  2. 2.CERENA / ISTUniversidade de LisboaPortugal

Personalised recommendations