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

Materials Science , Minerals

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