Dynamic data driven simulations in stochastic environments Academic Article

abstract

• 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.

authors

• Efendiev, Yalchin
• Lazarov, Raytcho

published proceedings

• COMPUTING

author list (cited authors)

• Douglas, C., Efendiev, Y., Ewing, R., Ginting, V., & Lazarov, R.

citation count

• 21

complete list of authors

• Douglas, C||Efendiev, Y||Ewing, R||Ginting, V||Lazarov, R

publication date

• January 2006

publisher

• Springer Nature  Publisher

published in

• Computing  Journal

keywords

• Dddas
• Mcmc
• Permeability
• Porous Media Flow
• Uncertainty

Digital Object Identifier (DOI)

• 10.1007/s00607-006-0165-3

start page

• 321

end page

• 333

volume

• 77

issue

• 4

URL

• http://dx.doi.org/10.1007/s00607-006-0165-3