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

Adriane Beatriz de Souza Serapião | Zendy; Antonio Carlos Bannwart | Zendy

Open Access

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

Author(s) -

Adriane Beatriz de Souza Serapião,

Antonio Carlos Bannwart

Publication year - 2012

Publication title -

journal of petroleum engineering

Language(s) - English

Resource type - Journals

eISSN - 2314-5005

pISSN - 2314-5013

DOI - 10.1155/2013/746315

Subject(s) - pressure drop , flow (mathematics) , volumetric flow rate , two phase flow , flow coefficient , petroleum engineering , scale (ratio) , computer science , data mining , environmental science , mechanics , geology , physics , quantum mechanics

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

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