Study On Monitoring And Defoaming Of Foam Carrying In Gas Pipeline Of Gas Drainage Well

The technology of foam drainage gas recovery is applied extensively in gas fields because of its easy operation,wide application,low cost,high effectiveness.But after the natural gas enters the gathering pipeline with foam,the foam will gather in pipeline,efficiency of separator may decline,manifold may block,pressure regulator may invalid.It is because that the foam is complex,flow pattern of mixed fluid in gathering pipeline is unstable,it is hard to monitor the foam and defoaming effects poorly.In order to ensure that production runs steadily and efficiently,conducting research on monitoring and control of foam carrying with gas in gas pipeline is significant for engineering applications.In this paper,designing and setting up a simulation device for gas gathering pipeline of foam drainage well to simulate of the flow of foam in gas gathering pipeline.Observing and analyzing the influence of different factors on the flow characteristics of the foam fluid in the pipeline and the influence on the foam volume and foam stability.Based on the principle of infrared laser monitoring,designing and setting up a pipeline foam flow monitoring device,and proposing foam flow calculation model for different flow patterns.The influencing factors of defoamer were determined through simulation experiments.The BP neural network model was established to predict the filling quantity of defoamer under different conditions.Based on the vortex shear,impact and centrifugal,gravity,the gas liquid separation device with bubble breaking function is designed and built,and the feasibility of the device and the optimal flow rate are verified by means of experiments.The results show:In the gas,liquid and foam mixed flow pipeline,with the increase of gas-liquid ratio in the range of 0.63?143.62,there will be four typical flow patterns of full flow,slug flow,annular flow and stratified flow.Foaming liquid concentration,temperature,pipe diameter and length of the pipeline will affect the foam flow and foam stability to varying degrees.The pipeline foam flow monitoring device works well,it can identify the bubble flow pattern in the tube by displaying the image and voltage value.Flow calculation model has a good adaptability,and the experimental data shows the error range is between 8.2%?15.1%.The BP neural network model is based on the gas flow rate,the concentration of foaming liquid,the temperature,the diameter of the pipe,the length of the pipe,the amount of foam in the tube and the pressure.The BP neural network model successfully predicts the amount of defoaming agent under the influence of multiple factors,and demonstrates good applicability and accuracy.The average relative error between the model training and the measured values is 2.53%,the average relative error between the predicted and measured values is 7.58%.The separation device model shows good bubble breaking function and separation effect.When the inlet flow is between 6.5L/min to 9.5 L/min,foam efficiency and separation efficiency were higher to 65%and 90%.Under the experimental conditions,the best flow of the device is 8 L/min.Separation effect is better when the separator is used with defoamer.The research achievements of this paper provide directive significance for foam flow monitoring,defoamer filling and separator designing.