Abstract
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.
The research presented in this paper is supported in part by the National Science Foundation Grants ACI-9984357, EIA-0103674, EIA-0120934, ANI-0335244, CNS- 0305495, CNS-0426354, IIS-0430826, ACI-9619020 (UC Subcontract 10152408), ANI-0330612, EIA-0121177, SBR-9873326, EIA-0121523, ACI-0203846, ACI-0130437, CCF-0342615, CNS- 0406386, CNS-0426241, ACI-9982087, CNS-0305495, NPACI 10181410, Lawrence Livermore National Laboratory under Grant B517095 (UC Subcontract 10184497), Ohio Board of Regents BRTTC BRTT02-0003, and DOE DE-FG03-99ER2537.
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Keywords
- 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.
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
(Ipars: Integrated parallel reservoir simulator) The University of Texas at Austin, http://www.ices.utexas.edu/CSM
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)
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)
Deshpande, P.M., Ramasama, K., Shukla, A., Naughton, J.F.: Caching multidimensional queries using chunks. ACM SIGMOD Record 27(2), 259–270 (1998)
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)
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)
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)
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)
Zhang, L., Parashar, M.: Seine: A dynamic geometry-based shared space interaction framework for parallel scientific applications. Concurrency and Computations: Practice and Experience (2006)
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)
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)
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)
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Parashar, M. et al. (2006). Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758532_52
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DOI: https://doi.org/10.1007/11758532_52
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