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[1]李庆,刘洪飞,金曼青,等.基于物理-数据融合的油嘴气液两相虚拟计量方法[J].油气储运,2024,43(10):1-14.
　Li Qing,Liu Hongfei,Jin Manqing,et al.Physical-Data Fusion for Virtual Metering of Gas-Liquid Two-Phase Flow in Oil Nozzles[J].Oil & Gas Storage and Transportation,2024,43(10):1-14.

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基于物理-数据融合的油嘴气液两相虚拟计量方法

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 43 期数: 2024年10期页码: 1-14 栏目: 出版日期: 2024-10-25

Title:: Physical-Data Fusion for Virtual Metering of Gas-Liquid Two-Phase Flow in Oil Nozzles

作者:: 李庆; 　刘洪飞; 　金曼青; 　张瑛; 　梁法春

Author(s):: Li Qing; Liu Hongfei; Jin Manqing; Zhang Ying; Liang Fachun

关键词:: 油嘴; 气液两相流; 流量计量; 差压; 温差; 深度神经网络

Keywords:: Oil Nozzle; Gas-Liquid Two-Phase Flow; Flow Rate Metering; Pressure Difference; Temperature Difference; Deep Neural Network

分类号:: TE832

文献标志码:: A

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和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，试验气相折算速度范围1.73~12.09 m/s，液相折算速度范围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，无需额外测量仪表投入，且数据采集和建模成本低，具有广阔的推广应用前景。

Abstract:: [Objective] The metering of oil well production is a critical basis for assessing the reserves and production capacity of oil wells, providing valuable references for resource development and management. The fluid extracted at the wellhead is generally a multiphase flow. Current physical metering methods require significant investment and involve complex processes, while virtual metering methods demand numerous monitoring parameters, making implementation challenging and computationally costly. [Methods] After passing through the choke valve, the extracted fluid experiences a temperature drop and pressure drop. To enhance the accuracy of single-well virtual metering systems and improve the ease of system maintenance, a new method for oil and gas metering was proposed by integrating pressure differential fluctuation characteristics with temperature difference signals in the choke valve process. The mechanism equation for choke valve flow, coupling differential pressure, flow rate, and mass gas content, was derived and established. A deep neural network was also constructed using 9 features, including temperature difference, average pressure difference, and standard deviation, as input parameters. Through data-driven approaches, the mass gas content was inversely predicted. Using a real 10 mm choke valve as the test subject, experimental testing was conducted on a gas-liquid two-phase flow loop. The range of the gas phase equivalent velocity was 1.73 to 12.09 m/s, and the liquid phase equivalent velocity ranged from 0.03 to 0.35 m/s. The tested flow patterns included stratified flow, wavy flow, slug flow, and annular flow. [Results] The gas content adaptability range of the flow metering equation was 0-100%, and it was not affected by changes in flow patterns, gas-liquid flow rates, or system pressure. Both the mass gas content and flow metering errors were within ±10%. [Conclusion] The choke valve gas-liquid two-phase virtual metering method based on the fusion of physical and data-driven approaches relies solely on the existing temperature and pressure measurement systems of the choke valve to achieve virtual metering of gas-liquid flow. It does not require information on storage, wellbore, or gathering and transportation networks, nor does it require additional metering instruments. The data collection and modeling costs are low, making it highly promising for broad application.

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更新日期/Last Update: 2024-08-15