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Modelling data-driven CO2 sequestration using distributed HPC cyberinfrastructure

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Published:02 August 2010Publication History

ABSTRACT

In this paper we lay out the computational challenges involved in effectively simulating complex phenomena such as sequestering CO2 in oil and gas reservoirs. The challenges arise at multiple levels: (i) the computational complexity of simulating the fundamental processes; (ii) the resource requirements of the computationally demanding simulations; (iii) the need for integrating real-time data (intensive) and computationally intensive simulations; (iv) and the need to implement all of these in a robust, scalable and extensible approach. We will outline the architecture and implementation of the solution we develop in response to these requirements, and discuss results to validate claims that our solution scales to effectively solve desired problem sizes and thus provides the capability to generate novel scientific insight.

References

  1. SAGA Papers, 2003--2010. http://saga.cct.lsu.edu/publications/papers/.Google ScholarGoogle Scholar
  2. S. Balay, K. Buschelman, W. D. Gropp, D. Kaushik, M. G. Knepley, L. C. McInnes, B. F. Smith, and H. Zhang. PETSc Web page, 2001. http://www.mcs.anl.gov/petsc.Google ScholarGoogle Scholar
  3. Cactus Framework. http://www.cactuscode.org.Google ScholarGoogle Scholar
  4. D. J. DePaolo et al. Basic research needs for geosciences:facilitating 21st century energy systems. Office of Basic Energy Sciences, U.S. Department of Energy, 2007.Google ScholarGoogle Scholar
  5. G. P. W. Don W. Green. Enhanced Oil Recovery, volume 6. 1998.Google ScholarGoogle Scholar
  6. R. Duff and Y. El Khamra. Real time simulation in grid environments: Communicating data from sensors to scientific simulations. In Digital Energy Conference and Exhibition. SPE, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  7. R. Duff and Y. El Khamra. A sensor and computation grid enabled engineering model for drilling vibration research. In MG '08: Proceedings of the 15th ACM Mardi Gras conference, pages 1--1, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. G. et al. SAGA: A Simple API for Grid Applications -- High-Level Application Programming on the Grid. Computational Methods in Science and Technology: special issue "Grid Applications: New Challenges for Computational Methods", 8(2), SC05, November 2005.Google ScholarGoogle Scholar
  9. G. Evensen. Data Assimilation: The Ensemble Kalman Filter. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. P. J. V. L. Gerrit Burgers and G. Evensen. Analysis scheme in the ensemble kalman filter. Monthly Weather Review, American Meteorological Society, 126, 1998.Google ScholarGoogle Scholar
  11. Y. Gu and D. S. Oliver. The ensemble kalman filter for continuous updating of reservoir simulation models. Journal of Engineering Resources Technology, 128(1):79--87, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  12. Y. Gu and D. S. Oliver. An iterative ensemble kalman filter for multiphase fluid flow data assimilation. SPE Journal, 12(4):438--446, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  13. P. H. Herschel L. Mitchell and G. Pellerin. Ensemble size, balance and model-error representation in an ensemble kalman filter. Monthly Weather Review, American Meteorological Society, 130, 2002.Google ScholarGoogle Scholar
  14. P. Houtekamer and H. L. Mitchell. Data assimilation using an ensemble kalman filter technique. Monthly Weather Review, American Meteorological Society, 126, 1998.Google ScholarGoogle Scholar
  15. S. Jha, H. Kaiser, Y. El Khamra, and O. Weidner. Design and implementation of network performance aware applications using saga and cactus. In Accepted for 3rd IEEE Conference on eScience2007 and Grid Computing, Bangalore, India., 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. R. E. Kalman. A new approach to linear filtering and prediction problems. 2005. http://www.cs.unc.edu/~welch/kalman/media/pdf/Kalman1960.pdf.Google ScholarGoogle Scholar
  17. Y. E. Khamra and S. Jha. Title: Developing Autonomic Distributed Scientific Applications: A Case Study From History Matching Using Ensemble Kalman-Filters. In Sixth International Conference on Autonomic Computing, 2009. ICAC '09 (Barcelona). IEEE, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. X. Li, C. White, Z. Lei, and G. Allen. Reservoir model updating by ensemble kalman filter-practical approaches using grid computing technology. In Petroleum Geostatistics 2007, Cascais, Portugal, August 2007.Google ScholarGoogle ScholarCross RefCross Ref
  19. H. Liu and M. Parashar, 2006. Accord: A Programming Framework for Autonomic Applications.Google ScholarGoogle Scholar
  20. A. Luckow, S. Jha, J. Kim, A. Merzky, and B. Schnor. Adaptive distributed replica-exchange simulations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1897):2595--2606, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  21. L. Marini. Geological Sequestration of Carbon Dioxide, Volume 11: Thermodynamics, Kinetics, and Reaction Path Modeling (Developments in Geochemistry), volume 11. 2007.Google ScholarGoogle Scholar
  22. B. S. Rajesh J. Pawar, Dongxiao Zhang and H. R. Westrich. Preliminary geologic modeling and flow simulation study of co2 sequestration in a depleted oil reservoir. NETL 2001 Conference, 2001.Google ScholarGoogle Scholar
  23. J. K. Shantenu Jha and Y. E. Khamra. Developing scientific applications with loosely-coupled sub-tasks, 2009.Google ScholarGoogle Scholar

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        cover image ACM Other conferences
        TG '10: Proceedings of the 2010 TeraGrid Conference
        August 2010
        177 pages
        ISBN:9781605588186
        DOI:10.1145/1838574

        Copyright © 2010 ACM

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        Publication History

        • Published: 2 August 2010

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