Uncertainty assessment for inverse problems in high dimensional spaces using particle swarm optimization and model reduction techniques

Global optimization methods including particle swarm optimization are usually used to solve optimization problems when the number of parameters is low (hundreds). Also, to be able to find a good solution typically involves multiple evaluations of the objective (or cost) function. Thus, both a large...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Mathematical and computer modelling Ročník 54; číslo 11; s. 2889 - 2899
Hlavní autori:	Fernández-Martínez, J.L., Mukerji, T., García-Gonzalo, E., Fernández-Muñiz, Z.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.12.2011 Elsevier
Predmet:	algorithms Calculus of variations and optimal control Exact sciences and technology Inverse problems Mathematical analysis Mathematics Methods of scientific computing (including symbolic computation, algebraic computation) Model reduction techniques Numerical analysis Numerical analysis in abstract spaces Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations oil fields principal component analysis PSO Sciences and techniques of general use system optimization Uncertainty uncertainty analysis Uncertainty Inverse problems Model reduction techniques PSO Transcendental equation Optimization method Stochastic method Algorithm Global solution Inverse problem Non linear equation Particle method Numerical analysis Applied mathematics Algebraic equation Variational calculus Cost function Simulation model Sampling Mathematical model Computer aided analysis Mathematical programming
ISSN:	0895-7177, 1872-9479
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	Global optimization methods including particle swarm optimization are usually used to solve optimization problems when the number of parameters is low (hundreds). Also, to be able to find a good solution typically involves multiple evaluations of the objective (or cost) function. Thus, both a large number of parameters and very costly forward evaluations hamper the use of global algorithms in inverse problems. In this paper, we address the first problem showing that the sampling can be performed in a reduced model space. The reduction of the dimension is accomplished in this case by the principal component analysis computed on a set of scenarios that are built based on prior information using stochastic simulation techniques. The use of a reduced base helps to regularize the inverse problem and to find a set of equivalent models that fit the data within a prescribed tolerance, allowing uncertainty analysis around the minimum misfit solution. We show the application of this idea to a history matching problem of a synthetic oil reservoir, using different members of the PSO family to perform sampling on the reduced model space.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0895-7177 1872-9479
DOI:	10.1016/j.mcm.2011.07.009