Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository

  • Manish Parashar
  • Vincent Matossian
  • Hector Klie
  • Sunil G. Thomas
  • Mary F. Wheeler
  • Tahsin Kurc
  • Joel Saltz
  • Roelof Versteeg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


Previous work in the Instrumented Oil-Field DDDAS project has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management. This work has led to the implementation of advanced multi-physics, multi-scale, and multi-block numerical models and an autonomic software stack for DDDAS applications. The stack implements a Grid-based adaptive execution engine, distributed data management services for real-time data access, exploration, and coupling, and self-managing middleware services for seamless discovery and composition of components, services, and data on the Grid. This paper investigates how these solutions can be leveraged and applied to address another DDDAS application of strategic importance – the data-driven management of Ruby Gulch Waste Repository.


Range Query Acid Rock Drainage Idaho National Laboratory Very Fast Simulated Annealing Subsurface Characterization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Parashar, M., Klie, H., Catalyurek, U., Kurc, T., Matossian, V., Saltz, J., Wheeler, M.: Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 805–812. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Parashar, M., Matossian, V., Bangerth, W., Klie, H., Rutt, B., Kurc, T., Catalyurek, U., Saltz, J., Wheeler, M.: Towards dynamic data-driven optimization of oil well placement. In: Proceedings of the Workshop on Distributed Data Driven Applications and Systems, International Conference on Computational Science 2005 (ICCS 2005), Atlanta, USA, May 2005, vol. 3514-3516, pp. 656–663. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Klie, H., Bangerth, W., Gai, X., Wheeler, M.F., Stoffa, P., Sen, M., Parashar, M., Catalyurek, U., Saltz, J., Kurc, T.: Models, methods and middleware for grid-enabled multiphysics oil reservoir management. In: Engineering with Computers. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Matossian, V., Bhat, V., Parashar, M., Peszynska, M., Sen, M., Stoffa, P., Wheeler, M.F.: Autonomic oil reservoir optimization on the grid. Concurrency and Computation: Practice and Experience 17, 1–26 (2005)CrossRefGoogle Scholar
  5. 5.
    Bangerth, W., Klie, H., Matossian, V., Parashar, M., Wheeler, M.F.: An autonomic reservoir framework for the stochastic optimization of well placement. Cluster Computing: The Journal of Networks, Software Tools, and Applications 8, 255–269 (2005)Google Scholar
  6. 6.
    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., Parashar, M.: A simulation and data analysis system for large scale,data-driven oil reservoir simulation studies. Concurrency and Computation: Practice and Experience. 17, 1441–1467 (2005)CrossRefGoogle Scholar
  7. 7.
    Parashar, M., Muralidhar, R., Lee, W., Wheeler, M., Arnold, D., Dongarra, J.: Enabling interactive and collaborative oil reservoir simulations on the grid. Concurrency and Computation: Practice and Experience 17, 1387–1414 (2005)CrossRefGoogle Scholar
  8. 8.
    Versteeg, R., Wangerud, K., et al.: Managing a capped acid rock drainage (ard) repository using semi-autonomous monitoring and modeling. In: ICARD 2006, St. Louis, Missouri (2006)Google Scholar
  9. 9.
    Wangerud, K., Versteeg, R., et al.: Insights into hydrodynamic and geochemical processes in a valley-fill ard waste-rock repository from an autonomous multi-sensor monitoring system. In: ICARD 2006, St. Louis, Missouri (2006)Google Scholar
  10. 10.
    (Ipars: Integrated parallel reservoir simulator) The University of Texas at Austin,
  11. 11.
    Zhang, X., Pan, T., Catalyurek, U., Kurc, T., Saltz, J.: Serving queries to multi-resolution datasets on disk-based storage clusters. In: Proceedings of 4th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2004), Chicago, IL (2004)Google Scholar
  12. 12.
    Weng, L., Catalyurek, U., Kurc, T., Agrawal, G., Saltz, J.: Servicing range queries on multidimensional datasets with partial replicas. In: Proceedings of the 5th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2005 (2005)Google Scholar
  13. 13.
    Deshpande, P.M., Ramasama, K., Shukla, A., Naughton, J.F.: Caching multidimensional queries using chunks. ACM SIGMOD Record 27(2), 259–270 (1998)CrossRefGoogle Scholar
  14. 14.
    Narayanan, S., Kurc, T., Catalyurek, U., Zhang, X., Saltz, J.: Applying database support for large scale data driven science in distributed environments. In: Proceedings of the Fourth International Workshop on Grid Computing (Grid 2003), Phoenix, Arizona, pp. 141–148 (2003)Google Scholar
  15. 15.
    Hastings, S., Langella, S., Oster, S., Saltz, J.: Distributed data management and integration: The mobius project. In: GGF Semantic Grid Workshop 2004, GGF, pp. 20–38 (2004)Google Scholar
  16. 16.
    Beynon, M.D., Kurc, T., Catalyurek, U., Chang, C., Sussman, A., Saltz, J.: Distributed processing of very large datasets with DataCutter. Parallel Computing 27, 1457–1478 (2001)zbMATHCrossRefGoogle Scholar
  17. 17.
    Parashar, M., Liu, H., Li, Z., Matossian, V., Schmidt, C., Zhang, G., Hariri, S.: Automate: Enabling autonomic grid applications. Cluster Computing: The Journal of Networks, Software Tools, and Applications, Special Issue on Autonomic Computing 9 (2006)Google Scholar
  18. 18.
    Zhang, L., Parashar, M.: Seine: A dynamic geometry-based shared space interaction framework for parallel scientific applications. Concurrency and Computations: Practice and Experience (2006)Google Scholar
  19. 19.
    Liu, H., Parashar, M.: Accord: A programming framework for autonomic applications. IEEE Transactions on Systems, Man and Cybernetics, Special Issue on Engineering Autonomic Systems (2006)Google Scholar
  20. 20.
    Chandra, S., Parashar, M., Yang, J., Zhang, Y., Hariri, S.: Investigating autonomic runtime management strategies for samr applications. International Journal of Parallel Programming 33, 247–259 (2005)CrossRefGoogle Scholar
  21. 21.
    Mann, V., Parashar, M.: DISCOVER: A computational collaboratory for interactive grid applications. In: Berman, F., Fox, G., Hey, T. (eds.) Grid Computing: Making the Global Infrastructure a Reality, pp. 727–744. John Wiley and Sons, Chichester (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Manish Parashar
    • 1
  • Vincent Matossian
    • 1
  • Hector Klie
    • 2
  • Sunil G. Thomas
    • 2
  • Mary F. Wheeler
    • 2
  • Tahsin Kurc
    • 3
  • Joel Saltz
    • 3
  • Roelof Versteeg
    • 4
  1. 1.TASSL, Dept. of Electrical & Computer EngineeringRutgers, The State University of New JerseyNew JerseyUSA
  2. 2.CSM, ICESThe University of Texas at AustinTexasUSA
  3. 3.Dept. of Biomedical InformaticsThe Ohio State UniversityOhioUSA
  4. 4.INLIdahoUSA

Personalised recommendations