NDLI: Comparison of five different ways to assimilate data for a simplistic weakly nonlinear parameter estimation problem

Content Provider	Springer Nature Link
Author	Mannseth, Trond
Copyright Year	2015
Abstract	Four non-iterative parameter estimation methods, applying four different ways to assimilate data, are compared to the (iterative) randomized maximum likelihood (RML) method on problems where the forward model is weakly nonlinear and simplistic. Both original and slightly modified versions of established data assimilation methods used for reservoir history matching are considered. Within the Gauss-linear regime, these methods are equivalent in the sense that they all sample correctly from the posterior pdf. The modification consists of replacing the global Kalman gain by a local gain similar to the one applied with RML. This modification, and the simplicity of the forward model considered, allows for asymptotic calculations, correct to lowest order in the quantity controlling the forward-model nonlinearity, to be performed for the non-iterative methods as well as for RML. Comparison of the resulting asymptotic parameter estimates explicitly reveals interesting relations between different ways to assimilate data, that goes beyond the Gauss-linear regime. Results from extensive numerical experiments with the modified methods and with the original methods support the asymptotic results when considering the mean results over a large number of randomized experimental settings where also the forward models vary. For randomized experiments with fixed forward model settings, trends from the asymptotic results were not visible in the numerical results. Although the nonlinear forward models considered are not closely related to reservoir models, the knowledge obtained should be useful also when the latter type of models are considered.
Starting Page	791
Ending Page	804
Page Count	14
File Format	PDF
ISSN	14200597
Journal	Computational Geosciences
Volume Number	19
Issue Number	4
e-ISSN	15731499
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-05-10
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Simultaneous data assimilation Sequential data assimilation Multiple data assimilation Iteration Non-linearity Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical Modeling and Industrial Mathematics Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics

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