%0 Journal Article %A Cui, Tiangang %A Fox, C. %A Nicholls, G. K. %A O'Sullivan, M. J. %D 2019 %I Begell House %K parallel MCMC, statistical inverse problems, geothermal modeling, uncertainty qualification %N 3 %P 295-310 %R 10.1615/Int.J.UncertaintyQuantification.2019029282 %T USING PARALLEL MARKOV CHAIN MONTE CARLO TO QUANTIFY UNCERTAINTIES IN GEOTHERMAL RESERVOIR CALIBRATION %U https://www.dl.begellhouse.com/journals/52034eb04b657aea,1b58af220d28d8e5,52b112ab4d0f97af.html %V 9 %X 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. %8 2019-06-27