Ultrasound recognition method for flow patterns in oil-gas-water slug flow based on RBF neural network

被引：0
|
作者：
Su Q. [1 ,2 ]
Xia Z. [1 ]
Liu Z. [1 ,2 ]
机构：
[1] School of Information Science and Engineering, School of Artificial Intelligence, Wuhan University of Science and Technology, Hubei, Wuhan
[2] Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Hubei, Wuhan
来源：
Huagong Jinzhan/Chemical Industry and Engineering Progress | 2024年 / 43卷 / 02期
关键词：
flow pattern recognition; multiphase flow; oil-gas-water slug flow; RBF neural network; transient response; ultrasound propagation transmission attenuation;
D O I：
10.16085/j.issn.1000-6613.2023-1219
中图分类号：
学科分类号：
摘要：
Flow pattern recognition plays a crucial role in the efficient operation and management of oil pipelines. However, the existing methods primarily focus on gas-liquid and oil-water two-phase flow, with limited accuracy in identifying flow patterns within the oil-gas-water slug flow segment. To address this limitation, this study proposed an ultrasound-based method for identifying flow patterns in the oil-gas-water slug flow segment using a radial basis function (RBF) neural network. The proposed method utilized the unique characteristics of phase distribution within the oil-gas-water slug flow segment and establishes a comprehensive set of 350 ultrasound test simulation models. By employing ultrasound transmission attenuation and reflection echo techniques, the response characteristics of the oil-gas-water slug flow segment within the pipeline were investigated. The transmission attenuation signals were then extracted to differentiate between the liquid film region, bubble entrainment region, and stable liquid slug region. To classify the flow patterns, the statistical features, such as the energy of reflected signal time series data, were extracted and utilized as inputs for the RBF neural network. The experimental results demonstrated that the proposed method achieves a high flow pattern recognition rate of 95.7% based on the ultrasound propagation mechanism and RBF neural network. This research provided a theoretical foundation for implementing flow pattern recognition of oil-gas-water slug flow in horizontal pipelines using ultrasound technology. The application of the RBF neural network-based recognition algorithm significantly enhanced the accuracy and efficiency of flow pattern identification, offering valuable insights for the effective operation and control of oil pipeline systems. © 2024 Chemical Industry Press Co., Ltd.. All rights reserved.
引用
收藏
页码：628 / 636
页数：8
相关论文
共 24 条
  • [1] YAQUB Muhammad Waqas, MARAPPAGOUNDER Ramasamy, RUSLI Risza, Et al., Flow pattern identification and measurement techniques in gas-liquid-liquid three-phase flow: A review, Flow Measurement and Instrumentation, 76, (2020)
  • [2] LUO Xiaming, HE Limin, LYU Yuling, Transition from slug flow to roll wave flow in oil-gas-water three-phase horizontal pipe, CIESC Journal, 61, 3, pp. 629-634, (2010)
  • [3] MANDHANE J M, GREGORY G A, AZIZ K., A flow pattern map for gas-liquid flow in horizontal pipes, International Journal of Multiphase Flow, 1, 4, pp. 537-553, (1974)
  • [4] WANG Shufan, ZHANG Hongquan, SARICA Cem, Et al., Experimental study of high-viscosity oil/water/gas three-phase flow in horizontal and upward vertical pipes, SPE Production & Operations, 28, 3, pp. 306-316, (2013)
  • [5] MOHMMED Abdalellah O, NASIF Mohammad S, AL-KAYIEM Hussain H, Et al., Measurements of translational slug velocity and slug length using an image processing technique, Flow Measurement and Instrumentation, 50, pp. 112-120, (2016)
  • [6] GUILIZZONI Manfredo, BACCINI Beatrice, SOTGIA Giorgio, Et al., Image-based analysis of intermittent three-phase flow, International Journal of Multiphase Flow, 107, pp. 256-262, (2018)
  • [7] MUKHERJEE Tanumoy, DAS Gargi, RAY Subhabrata, Sensor-based flow pattern detection—Gas-liquid-liquid upflow through a vertical pipe, AIChE Journal, 60, 9, pp. 3362-3375, (2014)
  • [8] HU Bin, Stewart Colin, HALE Colin P, Et al., Development of an X-ray computed tomography (CT) system with sparse sources: Application to three-phase pipe flow visualization, Experiments in Fluids, 39, 4, pp. 667-678, (2005)
  • [9] YANG Z, HU B, LANGSHOLT M, Et al., Experimental studies on void fraction in. slug of three-phase viscous oil flows in a near horizontal pipe, 17th International Conference on Multiphase Production Technology, pp. 255-267, (2015)
  • [10] HOFFMANN Rainer, JOHNSON George William, Measuring phase distribution in high pressure three-phase flow using gamma densitometry, Flow Measurement and Instrumentation, 22, 5, pp. 351-359, (2011)
123