Accelerating geostatistical simulations using graphics processing units (GPU)

  • Authors:

  • Pejman Tahmasebi;Muhammad Sahimi;GréGoire Mariethoz;Ardeshir Hezarkhani

  • Affiliations:

  • Department of Mining, Metallurgy and Petroleum Engineering, Amir Kabir University of Technology, Tehran 15875, Iran;Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-1211, USA;National Center for Groundwater Research and Training, School of Civil and Environmental Engineering, University of New South Wales, NSW 2052 Sydney, Australia;Department of Mining, Metallurgy and Petroleum Engineering, Amir Kabir University of Technology, Tehran 15875, Iran

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2012

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Abstract

Geostatistical simulations have become a widely used tool for modeling of oil and gas reservoirs and the assessment of uncertainty. One important current issue is the development of high-resolution models in a reasonable computational time. A possible solution is based on taking advantage of parallel computational strategies. In this paper we present a new methodology that exploits the benefits of graphics processing units (GPUs) along with the master-slave architecture for geostatistical simulations that are based on random paths. The methodology is a hybrid method in which different levels of master and slave processors are used to distribute the computational grid points and to maximize the use of multiple processors utilized in GPU. It avoids conflicts between concurrently simulated grid points, an important issue in high-resolution and efficient simulations. For the sake of comparison, two distinct parallelization methods are implemented, one of which is specific to pattern-based simulations. To illustrate the efficiency of the method, the algorithm for the simulation of pattern is adapted with the GPU. Performance tests are carried out with three large grid sizes. The results are compared with those obtained based on simulations with central processing units (CPU). The comparison indicates that the use of GPUs reduces the computation time by a factor of 26-85.