%0 Journal Article
%T Knowledge Discovery for Classification of Three-Phase Vertical Flow Patterns of Heavy Oil from Pressure Drop and Flow Rate Data
%A Adriane B. S. Serapi£żo
%A Antonio C. Bannwart
%J Journal of Petroleum Engineering
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/746315
%X 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. 1. Introduction The design of oil production pipelines involves evaluation of flow lines subject to multiphase flow of oil, water, and gas, where oscillations in pressure, temperature, and phase concentration typically occur. Furthermore, the phases usually flow on different geometrical distributions inside the pipe, named flow patterns. The identification of flow patterns is essential for the economic evaluation of the project, such as pressure drop and flow rate along the pipeline. These aspects are critical on offshore production conditions, where extensive distances and high costs are involved. Flow pattern identification is an important step to design separation equipments, slug catchers, gas lift operations, wellhead gathering systems, and production management and control. With the discovery of heavy oil reservoirs the lack of tools and methodologies for flow pattern identification deserves attention because the existing multiphase flow correlations are made for low API oils, where the oil-water mixture may be treated as a single liquid phase with average properties. However, for water-continuous flow of heavy oil below bubble-point, three distinct phases are present, that is, oil, water, and gas, thus making the traditional approach of flow pattern classification and pressure drop prediction in three-phase flow may have poor accuracy. Basically, there are two types of models for flow pattern prediction: empirical and mechanistic. Empirical models are related to experimental data, where flow pattern maps are experimentally determined and analyzed with respect to mathematical relations representing the boundaries between the flow pattern regions. These relations depend on the amount of experimental data used and on the coordinate system in which the data are presented. Mechanistic models are based on balance equations [1]. Notwithstanding, these models are formulated to describe single or two-phase flows, and they cannot be highly extended for oil-gas-water mixtures, when the
%U http://www.hindawi.com/journals/jpe/2013/746315/