Online recognition of the multiphase flow regime and study of slug flow in pipeline

Guo Liejin, Bai Bofeng, Zhao Liang, Wang Xin, Gu Hanyang
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Abstract

Multiphase flow is the phenomenon existing widely in nature, daily life, as well as petroleum and chemical engineering industrial fields. The interface structure among multiphase and their movement are complicated, which distribute random and heterogeneously in the spatial and temporal scales and have multivalue of the flow structure and state[1]. Flow regime is defined as the macro feature about the multiphase interface structure and its distribution, which is an important feature to describe multiphase flow. The energy and mass transport mechanism differ much for each flow regimes. It is necessary to solve the flow regime recognition to get a clear understanding of the physical phenomena and their mechanism of multiphase flow. And the flow regime is one of the main factors affecting the online measurement accuracy of phase fraction, flow rate and other phase parameters. Therefore, it is of great scientific and technological importance to develop new principles and methods of multiphase flow regime online recognition, and of great industrial background. In this paper, the key reasons that the present method cannot be used to solve the industrial multiphase flow pattern recognition are clarified firstly. Then the prerequisite to realize the online recognition of multiphase flow regime is analyzed, and the recognition rules for partial flow pattern are obtained based on the massive experimental data. The standard templates for every flow regime feature are calculated with self-organization cluster algorithm. The multi-sensor data fusion method is proposed to realize the online recognition of multiphase flow regime with the pressure and differential pressure signals, which overcomes the severe influence of fluid flow velocity and the oil fraction on the recognition. The online recognition method is tested in the practice, which has less than 10 percent measurement error. The method takes advantages of high confidence, good fault tolerance and less requirement of single sensor performance. Among various flow patterns of gas-liquid flow, slug flow occurs frequently in the petroleum, chemical, civil and nuclear industries. In the offshore oil and gas field, the maximum slug length and its statistical distribution are very important for the design of separator and downstream processing facility at steady state operations. However transient conditions may be encountered in the production, such as operational upsets, start-up, shut-down, pigging and blowdown, which are key operational and safety issues related to oil field development. So it is necessary to have an understanding the flow parameters under transient conditions. In this paper, the evolution of slug length along a horizontal pipe in gas-liquid flow is also studied in details and then an experimental study of flowrate transients in slug flow is provided. Also, the special gas-liquid flow phenomena easily encountered in the life span of offshore oil fields, called severe slugging, is studied experimentally and some results are presented.
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管道多相流型的在线识别与段塞流研究
多相流是广泛存在于自然界、日常生活以及石油、化工工业领域的现象。多相之间的界面结构及其运动较为复杂,在时空尺度上具有随机性和非均匀性,流动结构和状态具有多值性[1]。流型是多相界面结构及其分布的宏观特征,是描述多相流动的重要特征。每种流态的能量和质量输运机制差别很大。为了更好地认识多相流的物理现象及其机理,有必要解决流型识别问题。流型是影响相分数、流量等相参数在线测量精度的主要因素之一。因此,开发多相流流型在线识别的新原理和新方法具有重要的科学技术意义,具有重要的工业背景。本文首先阐明了目前方法不能用于工业多相流流型识别的主要原因。然后分析了实现多相流型在线识别的前提条件,并在大量实验数据的基础上得到了部分流型的识别规律。采用自组织聚类算法计算了各流型特征的标准模板。提出了多传感器数据融合方法,利用压力和差压信号实现多相流流态的在线识别,克服了流体流速和含油分数对识别的严重影响。该方法经实践验证,测量误差小于10%。该方法具有置信度高、容错性好、对单个传感器性能要求低等优点。在气液流动的各种流型中,段塞流在石油、化工、民用和核工业中经常发生。在海上油气田中,段塞最长长度及其统计分布对于分离器和下游处理设施的设计具有重要意义。然而,在生产过程中可能会遇到瞬态情况,例如作业中断、启动、关闭、清管和排污,这些都是与油田开发相关的关键操作和安全问题。因此,有必要了解瞬态条件下的流动参数。本文还详细研究了气液流动中水平管道段塞长度的变化规律,并对段塞流中流量瞬态进行了实验研究。对海上油田寿命周期中容易遇到的特殊气液流动现象——严重段塞流进行了实验研究,并给出了一些结果。
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