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 language | English |
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Pages (from-to) | 295-310 |
Number of pages | 16 |
Journal | International Journal for Uncertainty Quantification |
Volume | 9 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Keywords
- Geothermal modeling
- Parallel MCMC
- Statistical inverse problems
- Uncertainty qualification