Dynamic Data-Driven Contaminant Simulation

  • Craig C. Douglas
  • Yalchin Efendiev
  • Richard Ewing
  • Victor Ginting
  • Raytcho Lazarov
  • Martin J. Cole
  • Greg Jones
  • Chris R. Johnson
Conference paper

Summary

In this paper we discuss a numerical procedure for performing dynamic data driven simulations (DDDAS). In dynamic data driven simulations our goal is to update the solution as well as input parameters involved in the simulation based on local measurements. The updates are performed in time. In the paper we discuss (1) updating the solution using multiscale interpolation technique (2) recovering as well as updating initial conditions based on least squares approach (3) updating the permeability field using Markov Chain Monte Carlo techniques. We test our method on various synthetic examples.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    C.V. Deutsch and A. G. Journel, GSLIB: Geostatistical software library and user’s guide, 2nd edition, Oxford University Press, New York, 1998.Google Scholar
  2. 2.
    L.J. Durlofsky, Numerical calculation of equivalent grid block permeability tensors for heterogeneous porous media, Water Resour. Res. 27 (1991), 699–708.CrossRefGoogle Scholar
  3. 3.
    X.H. Wu, Y.R. Efendiev and T. Y. Hou, Analysis of upscaling absolute permeability, Discrete and Continuous Dynamical Systems, Series B 2 (2002), 185–204.MathSciNetMATHGoogle Scholar
  4. 4.
    Y. Efendiev A. Pankov, Numerical homogenization of nonlinear random parabolic operators, SIAM Multiscale Modeling and Simulation, 2 (2004), 237–268.MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    S.H. Lee, A. Malallah, A. Datta-Gupta and D. Higdon, Multiscale Data Integration Using Markov Random Fields, SPE Reservoir Evaluation and Engineering, February 2002.Google Scholar
  6. 6.
    C.C. Douglas, Y. Efendiev, R. Ewing, R. Lazarov, M.R. Cole, C.R. Johnson, and G. Jones, Virtual telemetry middleware for DDDAS, Computational Sciences ICCS 2003, P.M. Sllot et al., eds., 4 (2003), 279–288.Google Scholar
  7. 7.
    W.R. Gilks, S. Richardson and D.J. Spegelhalter, Markov Cain Monte Carlo in Practice, Chapman and Hall/CRC, London, 1996.Google Scholar
  8. 8.
    C.C. Douglas, C.E. Shannon, Y. Efendiev, R.E. Ewing, V. Ginting, R. Lazarov, M.J. Cole, G. Jones, C.R. Johnson, and J. Simpson, A Note on Data-Driven Contaminant Simulation, Lecture Notes in Computer Science, vol. 3038, Springer-Verlag, 2004, 701–708.Google Scholar
  9. 9.
    C.C. Douglas, Y. Efendiev, R.E. Ewing, V. Ginting, and R. Lazarov, Least-Square Approach for Initial Data Recovery in Dynamic Data-Driven Simulations, in preparation.Google Scholar
  10. 10.
    C.C. Douglas, Y. Efendiev, R.E. Ewing, V. Ginting, and R. Lazarov, Bayesian Approaches for Initial Data Recovery in Dynamic Data-Driven Simulations, in preparation.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Craig C. Douglas
    • 1
    • 2
  • Yalchin Efendiev
    • 3
  • Richard Ewing
    • 3
  • Victor Ginting
    • 3
  • Raytcho Lazarov
    • 3
  • Martin J. Cole
    • 4
  • Greg Jones
    • 4
  • Chris R. Johnson
    • 4
  1. 1.Department of Computer ScienceUniversity of KentuckyLexingtonUSA
  2. 2.Department of Computer ScienceYale UniversityNew HavenUSA
  3. 3.Texas A&M UniversityCollege StationUSA
  4. 4.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA

Personalised recommendations