基于物理-数据融合的油嘴气液两相虚拟计量方法  

Virtual metering of gas-liquid two-phase flow in oil nozzles based on physical-data fusion

作　　者：李庆[1] 刘洪飞 金曼青 张瑛 梁法春[2] LI Qing;LIU Hongfei;JIN Manqing;ZHANG Ying;LIANG Fachun(Petro China Planning & Engineering Institute;College of Pipeline and Civil Engineering, China University of Petroleum (East China);Engineering Technology Research Institute of Petro China Xinjiang Oilfield Company)

机构地区：[1]中国石油规划总院 [2]中国石油大学(华东)储运与建筑工程学院 [3]中国石油新疆油田公司工程技术研究院

出　　处：《油气储运》2024年第11期1285-1293,共9页Oil & Gas Storage and Transportation

基　　金：国家自然科学基金面上项目“油气混输管道水下微孔泄漏相分离特性及溢油形态演化机制”,52176165;中国石油集团直属院所基金项目“高气油比自喷生产油井复杂采出流体油嘴节流工况多相水力热力特征研究”,KJ2023-003。

摘　　要：【目的】油井产量计量是评估油井资源储量和产能的重要依据,可为资源开发和管理提供参考。井口采出介质一般为多相流体,现有物理计量方式投资过高且流程复杂,虚拟计量方式需要监测参数多,实施难度与计算成本大。【方法】采出流体通过油嘴节流后将产生温降与压降,为了提升单井虚拟计量系统的准确性与改善系统维护的便捷性,融合压差波动特征与温差信号提出油嘴节流实现油气计量新方法。推导建立了耦合差压、流量、质量含气率的油嘴节流流量机理方程。以节流温差、压差均值、标准差等9个特征为输入参数构建深度神经网络,通过数据驱动实现质量含气率的反演预测。以10 mm真实油嘴为测试对象,在气液两相流环道上开展试验测试,试验气相、液相折算速度范围分别为1.73~12.09 m/s、0.03~0.35 m/s,测试流型包括分层流、波浪流、段塞流以及环状流。【结果】流量计量方程含气率适应范围0~100%,且不受流型、气液流速、系统压力变化等影响,质量含气率及流量计量误差均低于±10%。【结论】基于物理-数据融合的油嘴气液两相虚拟计量方法仅依赖于油嘴现有温度、压力测量系统即可实现气液流量的虚拟计量,无需储藏、井筒以及集输管网信息,也无需投入额外测量仪表,且数据采集与建模成本低,具有广阔的推广应用前景。(图9,表3,参27)[Objective]Oil well production measurement is essential for assessing resource reserves and productivity,serving as a critical reference for resource development and management.The medium produced at the wellhead is typically a multiphase fluid.Current physical metering methods involve significant investment and complexity,while virtual metering requires numerous monitoring parameters,leading to implementation challenges and high computational costs.[Methods]The produced fluid undergoes both temperature and pressure drops as it passes through nozzle throttling.To enhance the accuracy of the single-well virtual metering system and the convenience of maintenance,a new method for oil and gas metering was proposed based on nozzle throttling,leveraging the characteristics of differential pressure fluctuations and temperature difference signals.The mechanism equation for nozzle throttling flow,incorporating differential pressure,flow rate,and gas mass fraction,was derived.A deep neural network was established using nine characteristics as input parameters,including throttling temperature difference,mean pressure difference and standard deviation,to facilitate data-driven inverse prediction of gas mass fraction.Taking a 10 mm real nozzle as the test object,the experimental tests were carried out on a gas-liquid two-phase flow loop.In the tests,the converted velocity range of gas phase was 1.73–12.09 m/s while that of liquid phase was 0.03–0.35 m/s.The flow patterns tested included stratified flow,wave flow,slug flow,and annular flow.[Results]The adaptive range of gas fraction was 0–100%for the mechanism equation,unaffected by changes in flow pattern,gas-liquid velocity,or system pressure.The errors in gas mass fraction and flow metering were within±10%.[Conclusion]The virtual metering of gas-liquid two-phase flow in oil nozzles based on physical-data fusion enables the virtual metering of gas and liquid flow using only the existing temperature and pressure measurement systems,without requiring information on stor

关 键 词：油嘴 气液两相流 流量计量 差压 温差 深度神经网络 

分 类 号：TE832[石油与天然气工程—油气储运工程]