Next Article in Journal
Quantitative Microbial Risk Analysis for Various Bacterial Exposure Scenarios Involving Greywater Reuse for Irrigation
Previous Article in Journal
An Analysis of the Potential Impact of Climate Change on the Structural Reliability of Drinking Water Pipes in Cold Climate Regions
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Water 2018, 10(4), 412; https://doi.org/10.3390/w10040412

Towards Improving the Efficiency of Bayesian Model Averaging Analysis for Flow in Porous Media via the Probabilistic Collocation Method

1,2
,
3,* , 1,2
and
4
1
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China
2
Department of Oil-Gas Field Development Engineering, College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
3
State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, SINOPEC Group, Beijing 050021, China
4
BIC-ESAT, College of Engineering, Peking University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 11 February 2018 / Revised: 22 March 2018 / Accepted: 29 March 2018 / Published: 1 April 2018
View Full-Text   |   Download PDF [19619 KB, uploaded 3 May 2018]   |  

Abstract

The characterization of flow in subsurface porous media is associated with high uncertainty. To better quantify the uncertainty of groundwater systems, it is necessary to consider the model uncertainty. Multi-model uncertainty analysis can be performed in the Bayesian model averaging (BMA) framework. However, the BMA analysis via Monte Carlo method is time consuming because it requires many forward model evaluations. A computationally efficient BMA analysis framework is proposed by using the probabilistic collocation method to construct a response surface model, where the log hydraulic conductivity field and hydraulic head are expanded into polynomials through Karhunen–Loeve and polynomial chaos methods. A synthetic test is designed to validate the proposed response surface analysis method. The results show that the posterior model weight and the key statistics in BMA framework can be accurately estimated. The relative errors of mean and total variance in the BMA analysis results are just approximately 0.013% and 1.18%, but the proposed method can be 16 times more computationally efficient than the traditional BMA method. View Full-Text
Keywords: model uncertainty; Bayesian model averaging; probabilistic collocation method model uncertainty; Bayesian model averaging; probabilistic collocation method
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Xue, L.; Dai, C.; Wu, Y.; Wang, L. Towards Improving the Efficiency of Bayesian Model Averaging Analysis for Flow in Porous Media via the Probabilistic Collocation Method. Water 2018, 10, 412.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top