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Dynamic Data Driven Simulations in Stochastic Environments

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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.

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Authors and Affiliations

  1. Department of Computer Science, University of Kentucky, 773 Anderson Hall, Lexington, KY, 40506-0046, USA

    C. Douglas

  2. Department of Computer Science, Yale University, P.O. Box 208285, New Haven, CT, 06520-8285, USA

    C. Douglas

  3. Institute for Scientific Computation and Department of Mathematics, Texas A&M University, 612 Blocker Hall, College Station, TX, 77843-3404, USA

    Y. Efendiev, R. Ewing & R. Lazarov

  4. Department of Mathematics, Colorado State University, 101 Weber Building, Fort Collins, CO, 80523-1874, USA

    V. Ginting

Authors
  1. C. Douglas
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  2. Y. Efendiev
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  3. R. Ewing
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  4. V. Ginting
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  5. R. Lazarov
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Corresponding author

Correspondence to C. Douglas.

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Douglas, C., Efendiev, Y., Ewing, R. et al. Dynamic Data Driven Simulations in Stochastic Environments. Computing 77, 321–333 (2006). https://doi.org/10.1007/s00607-006-0165-3

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  • DOI: https://doi.org/10.1007/s00607-006-0165-3

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