Dynamic data driven simulations in stochastic environments

C. Douglas*, Y. Efendiev, R. Ewing, V. Ginting, R. Lazarov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)321-333
Number of pages13
JournalComputing (Vienna/New York)
Issue number4
StatePublished - Jun 2006
Externally publishedYes


  • MCMC
  • Permeability
  • Porous media flow
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Computational Mathematics
  • Theoretical Computer Science
  • Numerical Analysis
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Dynamic data driven simulations in stochastic environments'. Together they form a unique fingerprint.

Cite this