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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)

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.

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.

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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

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