Knowledge Discovery for Classification of Three-Phase Vertical Flow Patterns of Heavy Oil from Pressure Drop and Flow Rate Data. (19th December 2012)

• Record Type: Journal Article
• Authors: Serapião, Adriane B. S.; Bannwart, Antonio C.
• Other Names: Galliero Guillaume Academic Editor.
• Is Part Of: Journal of petroleum engineering. Volume 2013(2013)
• Publication Date: 2012-12-19
• Subjects: Petroleum engineering -- Periodicals; Petroleum engineering; Periodicals; 622.338
• DOI: 10.1155/2013/746315
• Languages: English
• ISSNs: 2314-5005

Abstract

Abstract : 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.