Using parallel Markov chain Monte Carlo to quantify uncertainties in geothermal reservoir calibration

Tiangang Cui, C. Fox, G. K. Nicholls, M. J. O’sullivan

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Abstract

We introduce a parallel rejection scheme to give a simple but reliable way to parallelize the Metropolis-Hastings algorithm. This method can be particularly useful when the target density is computationally expensive to evaluate and the acceptance rate of the Metropolis-Hastings is low. We apply the resulting method to quantify uncertainties of inverse problems, in which we aim to calibrate a challenging nonlinear geothermal reservoir model using real measurements from well tests. We demonstrate the parallelized method on various well-test scenarios. In some scenarios, the sample-based statistics obtained by our scheme shows clear advantages in providing robust model calibration and prediction compared with those obtained by nonlinear optimization methods.

Original languageEnglish
Pages (from-to)295-310
Number of pages16
JournalInternational Journal for Uncertainty Quantification
Volume9
Issue number3
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Geothermal modeling
  • Parallel MCMC
  • Statistical inverse problems
  • Uncertainty qualification

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