Volume 2, Fora: Cavitation and Multiphase Flow; Fluid Measurements and Instrumentation; Microfluidics; Multiphase Flows: Work I 2014
DOI: 10.1115/fedsm2014-21849
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Experimental Study of Vertical Gas-Liquid Pipe Flow for Annular and Liquid Loading Conditions Using Dual Wire-Mesh Sensors

Abstract: In gas well production, liquid is produced in two forms, droplets entrained in the gas core and liquid film flowing on the tubing wall. For most of the gas well life cycle, the predominant flow pattern is annular flow. As gas wells mature, the produced gas flow rate reduces decreasing the liquid carrying capability initiating the condition where the liquid film is unstable and flow pattern changes from fully co-current annular flow to partially co-current annular flow. The measurement and visualization of annu… Show more

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Cited by 6 publications
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“…3). The use of dual WMS is for the purpose of the quantification of interfacial structure velocity at given experimental conditions, and is adopted by several other researchers in recent years [23][24][25][26][27][28]. However, the data from the first WMS is used to calculate time-averaged flow quantities such as liquid holdup, liquid film height, wetted wall perimeter and interfacial surface area.…”
Section: List Of Symbolsmentioning
confidence: 99%
“…3). The use of dual WMS is for the purpose of the quantification of interfacial structure velocity at given experimental conditions, and is adopted by several other researchers in recent years [23][24][25][26][27][28]. However, the data from the first WMS is used to calculate time-averaged flow quantities such as liquid holdup, liquid film height, wetted wall perimeter and interfacial surface area.…”
Section: List Of Symbolsmentioning
confidence: 99%
“…[33][34][35][36] They concluded that Hill's model 35 has the best performance in capturing the mentioned physical phenomena. To successfully transport particles in intermittent and stratified gas-liquid flow regimes, Vieira and Shirazi, 37 used artificial intelligence to predict the required minimum flow rates. They compared three machine learning algorithms: Support Vector Machine, Random Forest, and Extreme Gradient Boosting, and concluded that Random Forest provides the best training performance.…”
Section: Introductionmentioning
confidence: 99%