Research Article

A simulation and data analysis system for large-scale, data-driven oil reservoir simulation studies

  1. Tahsin Kurc1,*,
  2. Umit Catalyurek1,
  3. Xi Zhang1,
  4. Joel Saltz1,
  5. Ryan Martino2,
  6. Mary Wheeler2,
  7. Małgorzata Peszyńska3,
  8. Alan Sussman4,
  9. Christian Hansen4,
  10. Mrinal Sen5,
  11. Roustam Seifoullaev5,
  12. Paul Stoffa5,
  13. Carlos Torres-Verdin6,
  14. Manish Parashar7

Article first published online: 8 APR 2005

DOI: 10.1002/cpe.898

Issue

Concurrency and Computation: Practice and Experience

Concurrency and Computation: Practice and Experience

Special Issue: High-Performance Computing in Geosciences

Volume 17, Issue 11, pages 1441–1467, September 2005

Additional Information

How to Cite

Kurc, T., Catalyurek, U., Zhang, X., Saltz, J., Martino, R., Wheeler, M., Peszyńska, M., Sussman, A., Hansen, C., Sen, M., Seifoullaev, R., Stoffa, P., Torres-Verdin, C. and Parashar, M. (2005), A simulation and data analysis system for large-scale, data-driven oil reservoir simulation studies. Concurrency and Computation: Practice and Experience, 17: 1441–1467. doi: 10.1002/cpe.898

Author Information

  1. 1

    Biomedical Informatics Department, The Ohio State University, 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, U.S.A.

  2. 2

    Center for Subsurface Modeling, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th Street, ACE 5.324, Campus Mail C0200, Austin, TX 78712, U.S.A.

  3. 3

    Department of Mathematics, Kidder Hall 368, Oregon State University, Corvallis, OR 97331-4605, U.S.A.

  4. 4

    Department of Computer Science, University of Maryland, A.V. Williams Building, College Park, MD 20742, U.S.A.

  5. 5

    Institute for Geophysics, The University of Texas at Austin, 4412 Spicewood Springs Road, Building 600, Austin, TX 78758-8500, U.S.A.

  6. 6

    Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, 1 University Station, CO300, Austin, TX 78712-0228, U.S.A.

  7. 7

    Department of Electrical and Computer Engineering, Rutgers, State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, U.S.A.

*Biomedical Informatics Department, The Ohio State University, 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, U.S.A.

Publication History

  1. Issue published online: 8 APR 2005
  2. Article first published online: 8 APR 2005
  3. Manuscript Accepted: 17 FEB 2004
  4. Manuscript Revised: 5 JAN 2004
  5. Manuscript Received: 20 APR 2003

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Keywords:

  • oil reservoir simulation;
  • Grid computing;
  • data intensive computing;
  • data management

Abstract

The main goal of oil reservoir management is to provide more efficient, cost-effective and environmentally safer production of oil from reservoirs. Numerical simulations can aid in the design and implementation of optimal production strategies. However, traditional simulation-based approaches to optimizing reservoir management are rapidly overwhelmed by data volume when large numbers of realizations are sought using detailed geologic descriptions. In this paper, we describe a software architecture to facilitate large-scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. Copyright © 2005 John Wiley & Sons, Ltd.

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