Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Genetic algorithm assisted fuzzy iterative learning optimizer for automatic optimization of oil well placement under production constraints
KTH, School of Electrical Engineering (EES), Automatic Control.
2012 (English)In: Control Applications of Optimization, International Federation of Automatic Control , 2012, 223-228 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper a new optimization approach based on fuzzy systems and iterative learning is proposed where Genetic Algorithm (GA) employed to optimally determine fuzzy parameters. The method is appropriate for highly nonlinear and uncertain large scale systems such as optimal oil well placement. Well-placement is a crucial step in field development. However, the major difficulties of the problem are highly nonlinear dynamics of reservoir, well locations constraints and large number of decision variables. Therefore, in this paper, a new optimization method is proposed and employed to solve the problem. Fuzzy rule generation is done employing GA to avoid being stuck in local optima. Since fuzzy coefficients are considered as decision variables instead of well locations, number of optimization parameters reduces significantly. Simulation results show superior performance such as lower computational load and less number of simulator runs compared with ones obtained by previous methods.

Place, publisher, year, edition, pages
International Federation of Automatic Control , 2012. 223-228 p.
Series
IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670 ; 15
Keyword [en]
Evolutionary algorithm, Multi-objective Optimization, Oil well Placement, Optimization Method, Stochastic optimization, Automatic optimization, Optimization approach, Optimization parameter, Production constraints, Stochastic optimizations, Uncertain large-scale systems, Well placement, Decision making, Evolutionary algorithms, Iterative methods, Multiobjective optimization, Oil wells, Genetic algorithms
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-128974DOI: 10.3182/20120913-4-IT-4027.00045Scopus ID: 2-s2.0-84881032315ISBN: 978-390282314-4 (print)OAI: oai:DiVA.org:kth-128974DiVA: diva2:649387
Conference
15th IFAC Workshop on Control Applications of Optimization, CAO 2012; Rimini; Italy; 13 September 2012 through 16 September 2012
Note

QC 20130918

Available from: 2013-09-18 Created: 2013-09-17 Last updated: 2013-09-18Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ebadat, Afrooz
By organisation
Automatic Control
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 45 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf