泡排井采气管道携泡量监测与消泡研究

发布时间：2018-04-24 11:23

本文选题：泡排采气 + 泡沫管流　；参考：《西南石油大学》2017年硕士论文

【摘要】：泡沫排水采气工艺具有操作简便、适用范围广、成本低、效率高等优势,在气田开采中的应用非常广泛。然而,采出天然气携带大量泡沫进入管道后,会引发管道内积泡严重、分离器工作效率下降、堵塞汇管、调压阀失效等现象,这是由于泡沫自身性质复杂,管道内混合流体流型不稳定,泡沫监测技术缺乏,消泡剂加注量难确定,分离器工作效率不佳引起的。因此,为了保证生产的高效稳定,开展管道携泡量监测与消泡研究,具有重要的工程意义。本文通过搭建泡排井采气管道可视化实验装置,模拟气携泡沫在管道中的流动状态,观察并分析了不同气液比下不同因素对管道流体流动特征影响以及对泡沫量与泡沫稳定性的影响;基于红外激光监测原理,设计了管道携泡量监测装置,并针对不同流型提出了泡沫流量计算模型,编写泡沫流量计算程序;通过模拟实验确定了消泡剂加注量影响因素,建立BP神经网络模型,对消泡剂最优加注量进行了成功预测;基于旋流剪切、冲击作用以及离心、重力作用设计了具有消泡功能的气液分离装置,通过实验手段验证了装置的可行性和最佳使用流量。研究结果显示:在气、液、泡沫混合流动的管道中,伴随气液比在0.63～143.62范围内增大,混合流体会依次出现满管流、段塞流、环状流、分层流四种典型流型;起泡基液浓度、温度、管径、管长均对管中泡沫流量以及泡沫稳定性造成不同程度的影响;监测装置具有良好的工作效果,可通过采集卡显示图像以及电压值对管中流体流型进行识别,流量计算模型具有较好的精确性,实验数据表明,泡沫流量计算误差范围在8.2%～15.1%;以气流量、起泡基液浓度、温度、管径、管长、管中泡沫量、压力作为输入变量的BP神经网络模型展示了良好的收敛速度和精确性,模型训练结果与实测值平均相对误差为2.53%,预测结果与实测值之间平均相对误差为7.58%;分离装置模型表现出了较好的破泡效果与分离效果,在入口流量为6.5～9.5 L/min范围时,其破泡效率和分离效率均较高,可分别达到65%和90%以上,在实验条件下,装置的最佳使用流量为8 L/min,在管道中加注消泡剂时,该装置表现出了更好的分离效果。本文的研究成果对泡排采气现场管道中泡沫监测、消泡剂加注量优化以及现场分离装置设计具有一定指导意义。
[Abstract]:The foam drainage gas recovery process has the advantages of simple operation, wide application range, low cost and high efficiency, so it is widely used in gas field exploitation. However, if natural gas is produced to carry a large number of foam into the pipeline, it will lead to the serious accumulation of bubbles in the pipeline, the decline in the work efficiency of the separator, the blockage of the manifold and the failure of the pressure regulating valve. This is due to the complex nature of the foam itself. The flow pattern of the mixed fluid in the pipeline is unstable, the foam monitoring technology is lacking, the injection amount of defoamer is difficult to determine, and the work efficiency of the separator is not good. Therefore, in order to ensure the high efficiency and stability of production, it is of great engineering significance to carry out the monitoring and defoaming research of pipeline foam carrying capacity. In this paper, the flow state of gas carrying foam in pipeline is simulated by setting up a visual experimental device for gas production pipeline. The influence of different factors under different gas-liquid ratio on the flow characteristics of pipeline fluid and the influence on foam quantity and foam stability are observed and analyzed. Based on the principle of infrared laser monitoring, a monitoring device for pipe bubble carrying capacity is designed. According to different flow patterns, the foam flow calculation model is put forward, and the foam flow calculation program is compiled, and the influencing factors of defoamer dosage are determined through simulation experiments, and the BP neural network model is established, and the optimal injection amount of defoamer is successfully predicted. A gas-liquid separation device with defoaming function was designed based on swirl shear, impact, centrifugal and gravity effects. The feasibility and optimal flow rate of the device were verified by experimental means. The results show that in the gas, liquid and foam mixed flow pipe, with the increase of gas-liquid ratio in the range of 0.63 ~ 143.62, the mixing fluid will appear four typical flow patterns in turn: full pipe flow, slug flow, annular flow, stratified flow, bubble base liquid concentration, temperature, The diameter and length of the pipe have different effects on the foam flow rate and foam stability in the pipe. The monitoring device has a good working effect and can be used to identify the flow pattern of the fluid in the pipe by the image display and voltage value of the acquisition card. The experimental data show that the error range of foam flow calculation is 8.2and 15.1.The flow rate, bubble base fluid concentration, temperature, pipe diameter, pipe length and foam amount in pipe are calculated by gas flow rate, bubble base liquid concentration, temperature, pipe diameter, pipe length and foam amount in pipe. The BP neural network model with pressure as input variable shows good convergence speed and accuracy. The average relative error between the model training result and the measured value is 2.53, and the average relative error between the predicted result and the measured value is 7.58. Under the experimental conditions, the optimum flow rate of the device is 8 L / min. When the defoamer is added in the pipeline, the device shows better separation effect. The research results in this paper have a certain guiding significance for foam monitoring, defoamer injection optimization and field separation device design.
【学位授予单位】：西南石油大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TE377

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