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起伏湿气管道持液率分布规律及临界倾角模型 被引量:23

Liquid holdup distribution laws and critical inclination angle model of undulating wet gas pipelines
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摘要 起伏湿气管道在实际运行过程中,管道低洼处和上坡段容易形成积液,从而造成管输效率降低、管道压降增大、水合物生成、形成段塞流以及管道内腐蚀加剧等一系列问题,为此,对地形起伏湿天然气管道的积液规律进行分析。以某集气站集输管道为例,探究了地形起伏湿天然气管道沿线的液体分布情况,针对管内气体流速、管道倾角等因素对管道持液率的影响进行敏感性分析,得到该起伏湿气管道的积液规律以及某流量工况下管道不出现积液现象的临界管道倾角,并基于此建立了气体流速、管道倾角以及持液率之间的预测数学模型,将模型计算结果与软件模拟结果进行了对比分析,验证了预测模型的准确性。预测模型为湿气管道实际运行过程中的积液控制与安全运行提供了理论依据。 When undulating wet gas pipelines are in operation, liquid loading tends to occur at low-lying and ascent sections of pipelines, and consequently leads to a series of problems. These include the reduction of pipeline transmission efficiency, the increase of pipeline pressure drop, the generation of hydrate, the formation of slug flow and the aggravation of pipeline internal corrosion, etc. In this paper, therefore, the liquid loading laws of undulating wet gas pipelines were analyzed. The distribution situations of liquid along undulating wet gas pipelines were investigated by taking the gathering pipeline at a certain gas gathering station as an example. And sensitive analysis was performed to the effect of gas flow rates and pipe inclination angles on liquid holdup of the undulating wet gas pipeline. Consequently, the liquid loading laws of this undulating wet gas pipeline were understood and the critical inclination angle at which no liquid loading occurred at a certain flow rate was cognized. Afterwards, a mathematical model used to predict the relationship of liquid holdup, gas flow rate and pipe inclination angle was built up. Its accuracy was verified by comparing its calculation results with the simulation data of software. It provides a theoretical basis for the liquid loading control and safety of wet gas pipelines during their operation.
出处 《油气储运》 CAS 北大核心 2017年第2期177-184,共8页 Oil & Gas Storage and Transportation
基金 国家自然科学基金资助项目"立管内气液两相流动与海洋环境共同诱发立管振动耦合研究" 51404290
关键词 湿气管道 地形起伏 持液率 临界倾角 预测模型 wet gas pipelines, topographic relief, liquid holdup, critical inclination angle, prediction model
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