Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository*
Manish Parashar1, Vincent Matossian1, Hector Klie2, Sunil G. Thomas2, Mary F. Wheeler2, Tahsin Kurc3, Joel Saltz3, and Roelof Versteeg4
INL, Idaho, USA
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 ser vices 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 Foundat ion 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-0 426241, 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.
LNCS 3993, pp. 384-392.