Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6266
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dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorYüksel, H. Ertan-
dc.date.accessioned2021-09-11T15:35:32Z-
dc.date.available2021-09-11T15:35:32Z-
dc.date.issued2012en_US
dc.identifier.issn0920-4105-
dc.identifier.issn1873-4715-
dc.identifier.urihttps://doi.org/10.1016/j.petrol.2011.12.008-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6266-
dc.description.abstractIn drilling operations estimation of gas-liquid behavior such as flow patterns and liquid holdup is beneficial in terms of cost, time and efficient usage of resources for the well to be opened. There is a lack of research for hydraulic behavior of two phase fluids in annular geometries. One of the aims of this study is to observe the flow patterns experimentally in two phase eccentric annulus. The second aim is to detect the liquid holdup of these flows using digital image processing techniques instead of emprical correlations or mechanistic models. The last aim is to estimate the flow pattern and liquid holdup for two phase (air and water) flow in horizontal eccentric annulus. This is conducted by using artificial intelligence techniques rather than conventional mechanistic models. In this study, nearest neighbor algorithm, backpropagation neural networks, and decision trees are used as the artificial intelligence techniques. Flow is generalized by representing the flow patternsas superficial Reynolds numbers for both liquid and gas phase. The results showed that the back propagation neural network model provided the best results as an estimation model for flow pattern identification whereas regression decision tree had the best performance for liquid holdup determination. In air and water flow, 7 observed flow patterns are classified correctly with an accuracy of 90.38% and liquid holdup is estimated with an average absolute percent error of 17.06%. (C) 2011 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108M106]en_US
dc.description.sponsorshipThis study was funded through TUBITAK (The Scientific and Technological Research Council of Turkey) Project 108M106. The authors would like to send special thanks to Evren Ozbayoglu for providing the experimental setup and Reza Ettehadi Osgouei for helping in the data collection process in Middle East Technical University Multiphase Flow Loop.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Petroleum Science And Engineeringen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjecttwo phase flowen_US
dc.subjectmultiphase flowen_US
dc.subjectannulus flowen_US
dc.subjectimage processingen_US
dc.subjectartificial intelligenceen_US
dc.subjectartificial neural networksen_US
dc.titleAnalysis of Gas-Liquid Behavior in Eccentric Horizontal Annuli With Image Processing and Artificial Intelligence Techniquesen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume81en_US
dc.identifier.startpage31en_US
dc.identifier.endpage40en_US
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000302040500005en_US
dc.identifier.scopus2-s2.0-84855798555en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1016/j.petrol.2011.12.008-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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