RT Journal Article
ID 52b112ab4d0f97af
A1 Cui, Tiangang
A1 Fox, C.
A1 Nicholls, G. K.
A1 O'Sullivan, M. J.
T1 USING PARALLEL MARKOV CHAIN MONTE CARLO TO QUANTIFY UNCERTAINTIES IN GEOTHERMAL RESERVOIR CALIBRATION
JF International Journal for Uncertainty Quantification
JO IJUQ
YR 2019
FD 2019-06-27
VO 9
IS 3
SP 295
OP 310
K1 parallel MCMC
K1 statistical inverse problems
K1 geothermal modeling
K1 uncertainty qualification
AB 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.
PB Begell House
LK http://dl.begellhouse.com/journals/52034eb04b657aea,1b58af220d28d8e5,52b112ab4d0f97af.html