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ISSN 打印: 2152-5080

ISSN 在线: 2152-5099

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USING PARALLEL MARKOV CHAIN MONTE CARLO TO QUANTIFY UNCERTAINTIES IN GEOTHERMAL RESERVOIR CALIBRATION

卷 9, 册 3, 2019, pp. 295-310
DOI: 10.1615/Int.J.UncertaintyQuantification.2019029282
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摘要

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.

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对本文的引用
  1. Maclaren Oliver J., Nicholson Ruanui, Bjarkason Elvar K., O'Sullivan John P., O'Sullivan Michael J., Incorporating Posterior‐Informed Approximation Errors Into a Hierarchical Framework to Facilitate Out‐of‐the‐Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification, Water Resources Research, 56, 1, 2020. Crossref

  2. Scott S. W., O’Sullivan J. P., Maclaren O. J., Nicholson R., Covell C., Newson J., Guðjónsdóttir M. S., Bayesian Calibration of a Natural State Geothermal Reservoir Model, Krafla, North Iceland, Water Resources Research, 58, 2, 2022. Crossref

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