基于漂移理论的泡沫排水采气井井筒压降预测模型

Prediction model of wellbore pressure drop in foam drainage gas recovery well based on drift theory

泡沫排水采气(泡排)工艺因成本低、施工简单、见效快,在国内外各大气田中广泛应用,在众多排水采气工艺中扮演“主力军”作用。准确揭示泡排井井筒压降规律对于优化泡排工艺技术参数、提高泡沫排水采气工艺技术水平具有重要意义。由于漂移模型不划分流型,具有形式简单、便于工程计算的优点,是气液两相流发展的重要方向,而漂移模型的核心参数是漂移速度和分布系数。通过在30 mm内径的有机透明玻璃管中开展泡沫多相流实验,测试了不同倾斜角(0°~90°)液流速(0.01~0.20 m/s)、气流速(0~20m/s)及泡排剂浓度[(0~5000)×10^(-6)]下的压降规律,利用实验数据对气液两相漂移模型的漂移速度和分布系数进行了改进,提出了泡沫流动条件下的含气率计算方法,以此建立预测泡沫排水采气井筒压降的新模型。利用现场数据对新建模型进行评价的结果表明,新建模型的预测能力优于采油气工程中常用的经验模型和机理模型。

The technique of foam drainage for gas recovery is widely used in large domestic and foreign gas fields due to its low cost,simple construction and quick effect,and it plays a leading role in a number of drainage techniques for gas recovery.In fact,it is of great importance to accurately reveal the law of wellbore pressure drop in foam drainage gas recovery well for optimizing the technical parameters and improving the technical level of foam drainage technique.The drift model does not consider flow pattern,and is characterized by simple form and convenience for engineering calculation,thus providing a key direction for the development of gas-liquid two-phase flow.The core parameters of the model are drift velocity and distribution coefficient.In this paper,foam flow experiment was carried out in organic transparent glass tube(inner diameter:30mm)to test the pressure drop and void fraction at different inclinations(0°-90°),liquid velocities(0.01-0.20m/s),gas velocities(0-20m/s)and foam drainage agent concentrations[(0-5000)×10^(-6)].Experimental data were used to modify the correlations of the drift velocity and distribution coefficient of the drift model,and the calculation method of void fraction under foam flow conditions was also proposed.Based on the above,a new model for predicting the wellbore pressure drop in foam drainage well was established.According to the evaluation results of the new model based on field data,it is indicated that the new model has a prediction ability obviously superior to that of empirical and mechanism models that are commonly used in oil and gas production engineering.