Abstract
Effective geo-system management involves understanding of the interplay 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 dynamic, data-driven application systems (DDDAS) paradigm offers a promising framework to address this requirement. To achieve this goal, we have developed 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 dynamically adaptive strategies for subsurface characterization and management. These strategies have been applied to different aspects of subsurface management in strategically important application areas, including simulation-based optimization for the optimal oil well placement and the data-driven management of the Ruby Gulch Waste Repository. This paper summarizes the key outcomes and achievements of our work, as well as reports ongoing and future activities focused on uncertainty estimation and characterization.
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