Knowledge Discovery for Classification of Three-Phase Vertical Flow Patterns of Heavy Oil from Pressure Drop and Flow Rate Data
Joint Authors
Bannwart, Antonio C.
Serapião, Adriane B. S.
Source
Journal of Petroleum Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-12-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
This paper focuses on the use of artificial intelligence (AI) techniques to identify flow patterns acquired and recorded from experimental data of vertical upward three-phase pipe flow of heavy oil, air, and water at several different combinations, in which water is injected to work as the continuous phase (water-assisted flow).
We investigate the use of data mining algorithms with rule and tree methods for classifying real data generated by a laboratory scale apparatus.
The data presented in this paper represent different heavy oil flow conditions in a real production pipe.
American Psychological Association (APA)
Serapião, Adriane B. S.& Bannwart, Antonio C.. 2012. Knowledge Discovery for Classification of Three-Phase Vertical Flow Patterns of Heavy Oil from Pressure Drop and Flow Rate Data. Journal of Petroleum Engineering،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-495398
Modern Language Association (MLA)
Serapião, Adriane B. S.& Bannwart, Antonio C.. Knowledge Discovery for Classification of Three-Phase Vertical Flow Patterns of Heavy Oil from Pressure Drop and Flow Rate Data. Journal of Petroleum Engineering No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-495398
American Medical Association (AMA)
Serapião, Adriane B. S.& Bannwart, Antonio C.. Knowledge Discovery for Classification of Three-Phase Vertical Flow Patterns of Heavy Oil from Pressure Drop and Flow Rate Data. Journal of Petroleum Engineering. 2012. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-495398
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-495398