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Effective geo-system management involves understanding of the in- terplay between surface entities (e.g., locations of injection and production wells in an oil reservoir) and appropriately effecting subsurface characteristics. This in turn requires efficient integration of complex numerical models of the environment, optimization procedures, and decision making processes. The dy- namic, data-driven application systems (DDDAS) paradigm offers a promising framework to address this requirement. To achieve this goal, we have devel- oped advanced multi-physics, multi-scale, and multi-block numerical models and autonomic systems software for dynamic, data-driven applications systems. This work has enabled a new generation of data-driven, interactive and dynami- cally adaptive strategies for subsurface characterization and management. These strategies have been applied to different aspects of subsurface manage- ment in strategically important application areas, including simulation-based optimization for the optimal oil well placement and the data-driven manage- ment of the Ruby Gulch Waste Repository. This paper summarizes the key out- comes and achievements of our work, as well as reports ongoing and future ac- tivities focused on uncertainty estimation and characterization.

Dynamic Decision and Data-Driven Strategies for the Optimal Management of Subsurface Geo-Systems