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COMPUTING
2006

Dynamic Data Driven Simulations in Stochastic Environments

10 years 2 months ago
Dynamic Data Driven Simulations in Stochastic Environments
To improve the predictions in dynamic data driven simulations (DDDAS) for subsurface problems, we propose the permeability update based on observed measurements. Based on measurement errors and a priori information about the permeability field, such as covariance of permeability field and its values at the measurement locations, the permeability field is sampled. This sampling problem is highly nonlinear and Markov chain Monte Carlo (MCMC) method is used. We show that using the sampled realizations of the permeability field, the predictions can be significantly improved and the uncertainties can be assessed for this highly nonlinear problem. AMS Subject Classifications: 65N99.
Craig C. Douglas, Yalchin Efendiev, Richard E. Ewi
Added 11 Dec 2010
Updated 11 Dec 2010
Type Journal
Year 2006
Where COMPUTING
Authors Craig C. Douglas, Yalchin Efendiev, Richard E. Ewing, Victor Ginting, Raytcho D. Lazarov
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