Bootstrap confidence intervals for reservoir model selection techniques |
| |
| Authors: | Céline Scheidt Jef Caers |
| |
| Institution: | (1) Department of Critical Care Medicine, Sunnybrook and Women’s Health Sciences Centre, 2075 Bayview Avenue, M4N 3M5, Toronto, Ontario, Canada;(2) Interdepartmental Division of Critical Care, University of Toronto, Toronto, Ontario, Canada;(3) Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada;(4) Department of Critical Care Medicine, University Health Network, Toronto, Ontario, Canada |
| |
| Abstract: | Stochastic spatial simulation allows generation of multiple realizations of spatial variables. Due to the computational time
required for evaluating the transfer function, uncertainty quantification of these multiple realizations often requires a
selection of a small subset of realization. However, by selecting only a few realizations, one may risk biasing the P10, P50,
and P90 estimates as compared to the original multiple realizations. The objective of this study is to develop a methodology
to quantify confidence intervals for the estimated P10, P50, and P90 quantiles when only a few models are retained for response
evaluation. We use the parametric bootstrap technique, which evaluates the variability of the statistics obtained from uncertainty
quantification and constructs confidence intervals. Using this technique, we compare the confidence intervals when using two
selection methods: the traditional ranking technique and the distance-based kernel clustering technique (DKM). The DKM has
been recently developed and has been shown to be effective in quantifying uncertainty. The methodology is demonstrated using
two examples. The first example is a synthetic example, which uses bi-normal variables and serves to demonstrate the technique.
The second example is from an oil field in West Africa where the uncertain variable is the cumulative oil production coming
from 20 wells. The results show that, for the same number of transfer function evaluations, the DKM method has equal or smaller
error and confidence interval compared to ranking. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
