中图分类号: TE83
文献标识码: A
文章编号: 2095-7297(2015)01-0018-05
收稿日期: 2014-10-17
网络出版日期: 2015-02-20
版权声明: 2015 海洋工程装备与技术编辑部 版权所有
基金资助:
作者简介:
作者简介:叶晶(1984--),男,博士研究生,主要从事油气水多相流动研究.
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摘要
研究了两种不同上升立管结构下的严重段塞流特性,包括严重段塞流发生区域,立管底部压力波动,周期以及液塞长度,并将所得S型柔性立管中气水两相流流型图与典型的严重段塞流流型转变准则进行了比较.实验发现:两种立管结构下严重段塞流流动过程类似,S型柔性立管中的流动比竖直立管中多出压力二次增长过程;与竖直立管中的流动相比,S型柔性立管中流动的周期更长而相对液塞长度较小;S型柔性立管中严重段塞流发生区域比竖直立管中发生区域小.
关键词：
Abstract
The characteristics of severe slugging flow in two types of riser systems are studied, including occurrence region, pressure fluctuations at riser base, cycle time, and liquid slug length. The flow pattern map in S-shaped flexible riser is compared with the classical severe slugging flow transition criteria. The experimental results show that the severe slugging cycles are similar in two types of risers. However, there is a second growth on the pressure fluctuations in the S-shaped riser compared with the vertical riser. The period duration in the S-shaped riser is longer, but the liquid slug length is shorter. The occurrence region of severe slugging flow in the S-shaped riser is smaller than that in the vertical riser.
Keywords：
在海洋石油工业中经常采用集输-立管系统的管线形式将油气资源从油井传输至处理平台[1].总的来说,这种形式的管线通常包含两个主要部分:一个部分是曲折蜿蜒在海床表面的管线,从井口到达海底管汇,通常长达十余公里;另一部分是连接海底管汇和平台的上升立管,其高度随着海洋深度的不同而不同,同时也按照管道所能承受的应力等条件而采用不同的形式.在集输-立管系统中,以严重段塞流为代表的流动安全问题将会突出成为海洋油田开采中的一个重大问题[2].
对集输-立管系统中流型进行研究的重要任务就是识别需要避免的流型和确定它们的流动机理.前人研究[3-4]发现,这种流型只在流量较小且管线下倾的时候才能发生.下倾的管线更容易在其中产生分层流,这也是严重段塞流产生的一个重要机理性条件.在这种流型下,从油井中产生的气体会被积累在立管底部的液体阻塞在管线中,从而无法进入立管;而管线和立管中均形成长度极长的液塞.当立管中液体到达立管顶部时,被阻塞的气体将推动管线中的液塞离开管线进入立管;当管线中的液塞尾部达到立管底部时,气体开始进入立管并强烈喷发.严重段塞流有很长的液塞,长度从一个立管高度可以变化到多个立管高度,管线中同时伴随着强烈的压力波动以及较长周期的气液间歇流出.这样的流动过程对于分离器处理长液塞的能力和设备的抗冲击能力都提出了非常大的挑战.
竖直立管系统和S型柔性立管系统严重段塞流的研究虽然得到了广泛的关注[4-6],但很少有实验对两者产生的严重段塞流的特性进行比较分析.Tin[7]给出了光滑过渡的S型柔性立管中严重段塞流的流型划分,但也未将其与竖直立管中严重段塞流的特性进行详细的对比.本文的主要目的是通过竖直立管和S型柔性立管的对比实验,研究立管结构对严重段塞流的流型图以及周期和相对液塞长度等特征的影响.
本文实验是在西安交通大学动力工程多相流国家重点实验室油气水大型实验环路上进行的.本实验环道所采用的均为内径50 mm的不锈钢管,全长133 m,下倾管长度随着立管形式的不同而变化.当立管为竖直上升管时,下倾管长19 m,立管高度为15.3 m;S型柔性立管为内径50 mm的塑料软管,全长30 m,其中弯管顶部距立管底部高6.2 m,弯管底部距立管底部高2.2 m,立管出口距立管底部高15.3 m.下倾管的角度可以从-5°调整到+5°,在本文中为了比较方便,下倾管角度保持在-2°不变,因此,所有立管条件下,立管底部到气液分离器入口的竖直方向的高度均为15.3 m.
本文使用的工质为空气和水.用双螺杆压缩机将空气压缩至表压700 kPa,然后汇合由水箱经离心水泵抽出的自来水,并经气水混合装置混合输入水平环路及立管系统.水和空气在旋风分离器内分离,空气排空,水回水箱循环利用.
空气由3套孔板系统测量以适应不同的流量范围,3套孔板的测量误差分别为小孔板0.65%,中孔板1.7%和大孔板1.42%.水相由2台电磁流量计计量,精度为0.5级.为了控制流动的温度,在每相的流量计之前及立管出口安装了共3组热电偶.在立管顶部安装了1台伽马射线密度计用来测量立管出口的持液率变化.同时,在管线关键位置上安装了共19台压阻式压力变送器及3台差压变送器,精度分别为0.1%和0.05%.
实验中的信号通过NI PCI-6255M高速采集卡获得,采样频率为200 Hz.为了更好地对实验数据进行处理并获得准确的流动特征,每组采样时间设置为20 min.
本研究中,空气和水的流量覆盖了较广的范围,分别为:水相折算速度ULS=0.091.3 m/s;气相折算速度UGS=0.076 m/s.
通过总结文献中的流型划分准则,本研究把实验中观察到的流型分为:严重段塞流I(SS1),严重段塞流II(SS2),严重段塞流过渡流型(SST)以及稳定流型(ST).在立管中同时也发现泡状流和弹状流.然而,这两种流型对系统的危险性较小,所以把这两种流型都归为稳定流型.由于立管中严重段塞流研究较多,着重将本文所获得的S型柔性立管中的实验流型图与文献中的结果进行了对比.
图2和图3分别比较了本文实验结果与Taitel等[8],修正的Bøe准则[9-10],Montgomery[11]和Tin[7]的实验结果.各实验管道内径均为50 mm.其中Wang[10]与Taitel等[8]的准则为竖直立管,Montgomery[11]和Tin[7]的准则为S型柔性立管,下倾管角度均为-2°.
图2 S型柔性立管气水两相流型图与严重段塞流判定准则比较
Fig.2 Comparison of gas-water two-phase flow pattern map in S-shaped flexible riser with severe slugging occurrence criteria
图3 S型柔性立管气水两相流型图与Tin实验结果比较
Fig.3 Comparison of gas-water two-phase flow pattern map in S-shaped flexible riser with Tin's experimental results
图2中的实线表示Taitel等[8]给出的下倾管内分层流的转变准则,因为下倾管内的分层流是形成严重段塞流的一个重要条件.图2中的虚线表示改进后的Bøe准则,认为当发生严重段塞流时,立管内液体的积聚造成的压力上升速度必须大于集输管路中因上游输入气体造成的压力上升速度,由此可以推导出严重段塞流与其他流型的转换边界,而严重段塞流并不随立管高度大小而变化,判定标准为
ULS=
式中:ρGS为气相密度;R为气体常数;T为温度;ρL为液相密度;g为重力加速度;l为管线的长度;α为管线中的含气率;C为修正因子,并且C=2[10].这两条准则线内的区域认为是严重段塞流区域.从图2可见,本文实验数据与修正的Bøe准则线符合较好.图2中的点线为Montgomery给出的集输-S型柔性立管中的稳定性准则线,线内为不稳定流动区域,线外为稳定流动区域.由于水平管线很长且其中的空隙率预测并不准确,一些严重段塞流转变区的点也被包含进了准则线内.而且,各条准则线的上部也过高地预测了稳定流动的区域.
图3为本文实验数据与Tin实验数据比较示意图.Tin的实验采用的下倾段管长为60 m,立管高度为33 m.在气相调节阀门后液相混合点前,安装一缓冲容器,提高气体可压缩空间,而增大压缩空间相当于增大水平段管长.由式(1)可知,随着l的增大,严重段塞流区域向气相折算速度较大方向偏移.因此,如图3所示,Tin实验大于本实验中严重段塞流区域.其中气相和液相速度数表达式分别为:
Ngv=UGS
Nlv=ULS
式中:σL为表面张力系数,N/m.
图4给出了立管为竖直结构和S型柔性立管结构下气水两相严重段塞流发生区域的对比图.图中的数据点为本次实验所得的50 mm S型柔性立管中严重段塞流的数据点,蓝线为50 mm竖直立管严重段塞流区域,红线为50 mm S型柔性立管严重段塞流区域.从图中可以看到,竖直立管下严重段塞流区域比S型柔性立管要大很多.可见,S型柔性立管结构能够缩小发生严重段塞流的区域.
图4 两种结构下气水两相严重段塞流区域对比
Fig.4 Comparison of severe slugging occurrence region in two types of riser shapes
图5分别给出了竖直立管结构和S型柔性立管结构下空气-水两相压力波动曲线.不同结构下的压力变化规律主要有以下几点.
图5 发生严重段塞流时压力曲线对比(ULS=0.1 m/s,UGS=0.2 m/s)
Fig.5 Comparison of the pressure fluctuations of severe slugging (ULS=0.1 m/s, UGS=0.2 m/s)
首先,从图5中可以看出,竖直立管发生严重段塞流时,在液塞流出过程中,立管底部压力保持不变,形成压力平台期.S型柔性立管中发生严重段塞流时,在液塞流出过程中,P9[见图5(b)]也存在平台期,并在平台期结束时发生了二次增长过程.这是因为S型柔性立管存在一段下降管段,且在这段下降段中存在气体.在上一周期气体喷发后,S弯管下降段内和下倾管段类似,发生液体回流,从而产生一定的液位高度.当气体进入立管时,气体穿过S管下部上升管内液体后,推动S弯管下降段内液位下降,P9压力会进一步增大,当气体推动S弯管下降段内液位下降到最低点时,气体开始喷发.此后过程与竖直立管严重段塞流一致.
同时,当发生典型严重段塞流时,S型柔性立管中最大压力约为285 kPa,而竖直立管对应的最大压力约为250 kPa.这是因为S型柔性立管存在两个上升段和一个下降段,由前面的分析可知,下降段内液位高度小于下降段的竖直高度,且下降段内为层流,因此,S型柔性立管内最大压力大于竖直立管内最大压力.
图6描述了在50 mm竖直立管和S型柔性立管两种结构下,严重段塞流周期随气相折算速度和液相折算速度的变化规律.从图中可以看出,随着气相,液相折算速度的增大,严重段塞流周期减小.但在相同的气相折算速度和液相折算速度条件下,竖直立管结构内严重段塞流周期更小.
图6 两种结构下气水两相严重段塞流周期随各相折算速度变化
Fig.6 Variation of gas-water two-phase severe slugging flow periods with superficial velocity in two types of riser shapes
图7描述了竖直立管和S型柔性立管中出口液塞相对长度随气相折算速度变化规律.从图中可以看出,随着气相折算速度的增大,液塞长度减小.而在相同的气相,液相折算速度条件下,竖直立管内液塞长度大于S型柔性立管内液塞长度.
图7 出口液塞相对长度随气相折算速度变化
Fig.7 Variation of relative length of liquid slug with gas superficial velocity
本文实验研究了两种不同立管结构对严重段塞流特性的影响,实验结果与经典的严重段塞流流型转变准则及经典流型图符合较好.研究结果表明:竖直立管中严重段塞流发生区域要大于S型柔性立管.由于S型柔性立管长度上比竖直立管要长,因此相对液塞长度也比竖直立管中要小.但是一旦在S型柔性立管中发生严重段塞流,其周期将比相同气液流量下的竖直立管中的周期要长.
The authors have declared that no competing interests exist.
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In the offshore petroleum industry, it is important to recognize the severe slugging in multiphase flow patterns in oil and gas transportation through a pipeline–riser system. After analyzing the inadaptability of the existing methods, we proposed a simple and practical method of measuring the multiphase flow patterns based on pressure fluctuations. Our recognition was carried out in three steps. First, the outlet pressure signals were selected and processed because of their accessibility to practical applications in oil fields. Second, statistical and principal component analysis were performed on the sampled signals to obtain the clear interrelations between the signals and flow patterns and to extract useful features for forming flow pattern clusters for classification in the feature space. Finally, machine learning was applied to the clusters for constructing classifiers to predict the flow patterns automatically. The experimental results from a small-scale flow loop and an offshore petroleum field show that the proposed method is feasible and effective for recognizing the multiphase flow patterns in the pipeline–riser system.
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ABSTRACT At constant inflow conditions, large-amplitude pressure and flow rate fluctuations may occur in a pipeline–riser system operating at relatively low liquid and gas flow rates. This cyclic flow instability has been referred to as severe slugging. This study is an experimental, theoretical and numerical investigation of severe slugging in a relatively long pipeline–riser system. The experiments were carried out in a 65 m long, 50.8 mm diameter horizontal steel pipeline connected to a 35 m long, 50.8 mm diameter Perspex pipeline which is inclined to -2.54° from the horizontal, followed by a 15.5 m high, 45 mm vertical PVC riser operating at atmospheric end pressure. The experimental facility also included a 250 l gas buffer vessel, placed upstream of the pipeline, to obtain extra pipeline compressibility. Air and water were used as the experimental fluids. Five types of flow regimes were found and characterized based on visual observation and on the measured pressure drop over the riser. It was found that transient slugs were generated in the pipeline upstream of the riser base and they effectively contributed to the initial blockage of the riser base. An existing model for the prediction of the flow behaviour in the pipeline–riser system was modified. The modified model, which was tested against new experimental results obtained in this study, showed a better performance than previously published models. Numerical simulations were also performed using a one-dimensional two-fluid model. A good agreement between the numerical simulations and the experimental data was found.
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