Table of Contents
Journal of Petroleum Engineering
Volume 2013 (2013), Article ID 746315, 8 pages
Research Article

Knowledge Discovery for Classification of Three-Phase Vertical Flow Patterns of Heavy Oil from Pressure Drop and Flow Rate Data

1DEMAC, IGCE, UNESP, CP 178, 13506-900 Rio Claro, SP, Brazil
2DEP, FEM, UNICAMP, CP 6122, 13081-970 Campinas, SP, Brazil

Received 26 August 2012; Revised 20 November 2012; Accepted 21 November 2012

Academic Editor: Guillaume Galliero

Copyright © 2013 Adriane B. S. Serapião and Antonio C. Bannwart. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.