Towards dynamic data-driven management of the ruby gulch waste repository

  • Authors:

  • Manish Parashar;Vincent Matossian;Hector Klie;Sunil G. Thomas;Mary F. Wheeler;Tahsin Kurc;Joel Saltz;Roelof Versteeg

  • Affiliations:

  • TASSL, Dept. of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, New Jersey;TASSL, Dept. of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, New Jersey;CSM, ICES, The University of Texas at Austin, Texas;CSM, ICES, The University of Texas at Austin, Texas;CSM, ICES, The University of Texas at Austin, Texas;Dept. of Biomedical Informatics, The Ohio State University, Ohio;Dept. of Biomedical Informatics, The Ohio State University, Ohio;INL, Idaho

  • Venue:
  • ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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.