In the presented work we used a new approach for facies simulation, the Adaptive Plurigaussian Simulation method, in a “big-loop” history matching framework in order to generate an ensemble of facies realizations using and obeying the hard data coming from the well logs (the probability cubes) for a real field case in the North Sea. Using the IES we have conditioned/updated the prior ensemble using the production data and RFTs over a certain area of interest. The study shows successfully conditioning the facies realizations on production data, while honouring the hard data from well observations and the geological concept in the prior facies ensemble. The final ensemble of updated parameters was run in prediction mode to check the quality of our history matched facies models. The posterior ensemble of production profiles has a reduced uncertainty spread and has a mean closer to the observed value for most of the production history.


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  1. Chen, Yan, and Dean S.Oliver
    . “Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification.” Computational Geosciences17.4 (2013): 689–703.
    [Google Scholar]
  2. Evensen, Geir
    “Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics” Journal of Geophysical Research99.C5 (1994): 10143–10162.
    [Google Scholar]
  3. Evensen, Geir, et al.
    . “Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model.” Monthly Weather Review124.1 (1996): 85–96.
    [Google Scholar]
  4. Hanea, R. G., et al.
    Geologically Realistic Facies Updates for a North Sea Field.” 76th EAGE Conference and Exhibition 2014. 2014.
    [Google Scholar]
  5. Sebacher, B. et al.
    “An adaptive plurigaussian truncation scheme for geological uncertainty quantification using EnKF.” Under review Computational Geosciences, 2014
    [Google Scholar]
  6. Seiler, Alexandra, et al.
    “Structural surface uncertainty modeling and updating using the ensemble Kalman filter.” SPE Journal15.4 (2010): 1062–1076.
    [Google Scholar]
  7. Skjervheim, Jan-Arild, and GeirEvensen
    . “An ensemble smoother for assisted history matching.” SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2011.
    [Google Scholar]
  8. Zachariassen, E., et al.
    Integrated work flow for model update using geophysical monitoring data.” 73rd EAGE Conference & Exhibition. 2011.
    [Google Scholar]

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