1887

Abstract

Summary

Modelling a physical system such as oil reservoir, however accurate, is subject to uncertainty due to an unrealistic assumption about the model, uncertainty in measured data, and computer model incapability.

A realistic assessment of all sources of uncertainty is a challenging task, especially in oil and gas industry. On the other hand, unrealistic assumptions about model/data can lead to biased estimation of model parameters in a history matching progress. It may also be that the practitioners fail to reliably predict the true model behaviour and oilfield properties in case the uncertainty is not modelled appropriately.

In this paper, we model the uncertainty using two hierarchical models, maximum likelihood model and a full Bayesian hierarchical model. Moreover, we examine the predictive capability of our real reservoir model based on the modelled uncertainty with regards to the true model.

Doing multiple history match trials, a full hierarchical model approach yields better results for our case study than the maximum likelihood approach.

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/content/papers/10.3997/2214-4609.201701024
2017-06-12
2020-03-29
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References

  1. Brier, G.W.
    [1950]. Verification of forecasts expressed in terms of probability. Monthly weather review, 78(1), pp.1–3.
    [Google Scholar]
  2. Christie, M.A., Glimm, J., Grove, J.W., Higdon, D.M., Sharp, D.H. and Wood-Schultz, M.M.
    [2005]. Error analysis and simulations of complex phenomena. Los Alamos Science, 29(6).
    [Google Scholar]
  3. Christie, M., MacBeth, C. and Subbey, S.
    [2002]. Multiple history-matched models for Teal South. The Leading Edge, 21(3), pp.286–289
    [Google Scholar]
  4. Kennedy, M.C. and O’Hagan, A.
    [2001]. Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3), pp.425–464.
    [Google Scholar]
  5. Sambridge, M.
    [1999]. Geophysical inversion with a neighbourhood algorithmöII. Appraising the ensemble. Geophys. J. Int, 138, pp.727–746.
    [Google Scholar]
  6. Spall, J.C. and Garner, J.P.
    [1990]. Parameter identification for state-space models with nuisance parameters. IEEE Transactions on Aerospace and Electronic Systems, 26(6), pp.992–998.
    [Google Scholar]
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