Xin Li, Z. Lei, C.D. White, G. Allen, G. Qin, and F.T.-C. Tsai (USA)
Grid computing, Ensemble Kalman Filter, Model inversion
Ensemble Kalman Filter (EnKF) uses a randomized ensemble of subsurface models for performance esti mation. However, the complexity of geological models and the requirement of a large number of simulation runs make routine applications extremely difficult due to expensive computation cost. Grid computing tech nologies provide a cost-efficient way to combine geo graphically distributed computing resources to solve large-scale data and computation intensive problems. We design and implement a grid-enabled EnKF solu tion to ill-posed model inversion problems for subsur face modeling. It has been integrated into the Res Grid, a problem solving environment aimed at man aging distributed computing resources and conducting subsurface-related modeling studies. Two use cases in reservoir studies indicate that the enhanced ResGrid efficiently performs EnKF inversions to obtain rela tively accurate, uncertainty-ware predictions on reser voir production. This grid-enabled EnKF solution is also being applied for data assimilation of large-scale groundwater hydrology nonlinear models.
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