NDLI: Prediction using numerical simulations, a Bayesian framework for uncertainty quantification and its statistical challenge

Content Provider	IEEE Xplore Digital Library
Author	Glimm, J. Lee, Y. Ye, K.Q. Sharp, D.H.
Copyright Year	2003
Description	Author affiliation: Dept. of Appl. Math. & Stat., State Univ. of New York, Stony Brook, NY (Glimm, J.; Lee, Y.; Ye, K.Q.)
Abstract	Uncertainty quantification is essential in using numerical models for prediction. While many works focused on how the uncertainty of the inputs propagate to the outputs, the modeling errors of the numerical model were often overlooked. In our Bayesian framework, modeling errors play an essential role and were assessed through studying numerical solution errors. The main ideas and key concepts will be illustrated through an oil reservoir case study. In this study, inference on the input has to be made from the output. Bayesian analysis is adopted to handle this inverse problem, then combine it with the forward simulation for prediction. The solution error models were established based on the scale-up solutions and fine-grid solutions. As the central piece of our framework, the robustness of these error models is fundamental. In addition to the oil reservoir computer codes, we will also discuss the modelling of solution error of shock wave physics. Although the framework itself is simple, there is many statistical challenges which include optimal dimension of the error model, trade-off between sample size and the solution accuracy. These challenges are also discussed
Starting Page	380
Ending Page	385
File Size	1865207
Page Count	6
File Format	PDF
ISBN	0769519970
DOI	10.1109/ISUMA.2003.1236189
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Numerical simulation Bayesian methods Uncertainty Computer errors Numerical models Petroleum Hydrocarbon reservoirs Inverse problems Computational modeling Analytical models
Content Type	Text
Resource Type	Article

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