Application Of The Gamma Radiation Method In
Analysing The Effect Of Liquid Viscosity And Flow
Variables On Slug Frequency In High Viscosity
Oil-gas Horizontal Flow
Author(s)
C. N. Okezue
Abstract
Published experimental observations of high viscosity liquid-gas horizontal
flows have indicated that hydrodynamic slug flow is the commonest flow regime
encountered over a wide range of flow conditions. Since hydrodynamic slugging
generates high pressure fluctuations capable of damaging production pipelines, it
is necessary to understand the complex characteristics of high viscosity slug flow
regime through the analysis of one of its key parameters namely, slug frequency.
Investigating slug parameters is crucial, especially when existing slug flow
models used in the Petroleum Industry are not suitable for predicting the
behaviour of high viscosity oil-gas flow. This is because most existing empirical
correlations for predicting slug parameters are based on low viscosity liquid-gas
flows which have different hydrodynamic features compared to high viscosity
liquid-gas flows.
In Cranfield University, an experimental investigation of high viscosity oil-air
flow was performed to study the effect of liquid viscosity and flow variables on
hydrodynamic slug flow. Experiments were conducted at liquid superficial
velocities (Vso) and gas superficial velocities (Vsg) ranging from 0.06 m/s to
2.0 m/s and 0.0 m/s to 4.0 m/s respectively. For each experimental run, a singlebeam
gamma densitometer was used to measure slug frequency. The effect of
liquid viscosity (μ) on the measured slug frequency was investigated for the
range of 1100 cP to 4000 cP. When compared against measured high viscosity
slug frequency data, none of the existing empirical correlations used were able to
produce accurate predictions. The predicted results tend to diverge from
experimental data by several orders of magnitude. Given this context, a new
Keywords
gamma beam densitometer, two-phase flow, hydrodynamic slug flow regime, slug frequency, correlation, multiple-linear regression
Related Book
Other papers in this volume
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