Authors
Hamid Bazargan, Mike Christie, Ahmed H Elsheikh, Mohammad Ahmadi
Publication date
2015/12/31
Journal
Advances in Water Resources
Volume
86
Pages
385-399
Publisher
Elsevier
Description
Markov Chain Monte Carlo (MCMC) methods are often used to probe the posterior probability distribution in inverse problems. This allows for computation of estimates of uncertain system responses conditioned on given observational data by means of approximate integration. However, MCMC methods suffer from the computational complexities in the case of expensive models as in the case of subsurface flow models. Hence, it is of great interest to develop alterative efficient methods utilizing emulators, that are cheap to evaluate, in order to replace the full physics simulator. In the current work, we develop a technique based on sparse response surfaces to represent the flow response within a subsurface reservoir and thus enable efficient exploration of the posterior probability density function and the conditional expectations given the data.
Polynomial Chaos Expansion (PCE) is a powerful tool to quantify …
Scholar articles
H Bazargan, M Christie, AH Elsheikh, M Ahmadi - Advances in Water Resources, 2015