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

doi:10.16085/j.issn.1000-6613.2023-1219

Abstract

Abstract:

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.

Key words: multiphase flow, transient response, oil-gas-water slug flow, ultrasound propagation transmission attenuation, RBF neural network, flow pattern recognition

摘要：

石油管道内的多相流流型识别主要集中在气液两相流和油水两相流方向且准确识别流型范围有限，为了解决油气水段塞流流型识别问题，本文提出一种基于RBF神经网络和超声传播规律的油气水段塞流流型识别方法。根据油气水段塞流的相分布特点，建立了流型识别超声测试仿真模型。采用超声透射衰减技术和反射回波技术研究水平管道油气水三相段塞流超声响应特性，提取透射衰减信号区分段塞流液膜区、气泡夹带区和稳定液塞区。利用反射信号时间序列数据中的回波能量等统计特征，通过RBF神经网络对油气水段塞流进行流型识别。结果表明，基于超声传播机理以及RBF神经网络三相段塞流流型识别率为95.7%。基于RBF神经网络的流型识别算法研究为超声技术实现水平管油气水段塞流流型识别提供了理论基础。

关键词: 多相流, 瞬态响应, 油气水段塞流, 超声衰减, RBF网络, 流型识别

CLC Number:

TE81

SU Qian, XIA Zhifei, LIU Zhenxing. Ultrasound recognition method for flow patterns in oil-gas-water slug flow based on RBF neural network[J]. Chemical Industry and Engineering Progress, 2024, 43(2): 628-636.

苏茜, 夏志飞, 刘振兴. 基于RBF的油气水段塞流流型超声识别方法[J]. 化工进展, 2024, 43(2): 628-636.

Figures/Tables 13

References 24

15	ABBAGONI Baba Musa, YEUNG Hoi. Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network[J]. Measurement Science and Technology, 2016, 27(8): 084002.
16	FIGUEIREDO M M F, GONCALVES J L, NAKASHIMA A M V, et al. The use of an ultrasonic technique and neural networks for identification of the flow pattern and measurement of the gas volume fraction in multiphase flows[J]. Experimental Thermal and Fluid Science, 2016, 70: 29-50.
17	赵德喜, 曹学文, 张宇航, 等. 基于超声波技术的水平管气液两相流流型识别方法[J]. 油气储运, 2014, 33(2): 165-171.
	ZHAO Dexi, CAO Xuewen, ZHANG Yuhang, et al. Identification method of gas-liquid two-phase flow pattern of horizontal pipe based on ultrasonic technology[J]. Oil & Gas Storage and Transportation, 2014, 33(2): 165-171.
18	LIU Weiling, TAN Chao, DONG Feng. Doppler spectrum analysis and flow pattern identification of oil-water two-phase flow using dual-modality sensor[J]. Flow Measurement and Instrumentation, 2021, 77: 101861.
19	ZHANG Shuo, SHI Xuewei, TAN Chao, et al. Flow regimes identification of gas-water two-phase flow using conductance and continuous wave ultrasonic Doppler sensors[C]//2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). May 17-20, 2021, Glasgow, United Kingdom. IEEE, 2021: 1-6.
20	SU Qian, LI Jie, LIU Zhenxing. Flow pattern identification of oil-water two-phase flow based on SVM using ultrasonic testing method[J]. Sensors, 2022, 22(16): 6128.
21	YU Han, TAN Chao, DONG Feng. Measurement of oil fraction in oil-water dispersed flow with swept-frequency ultrasound attenuation method[J]. International Journal of Multiphase Flow, 2020, 133: 103444.
22	THAKER Jignesh, BANERJEE Jyotirmay. Characterization of two-phase slug flow sub-regimes using flow visualization[J]. Journal of Petroleum Science and Engineering, 2015, 135: 561-576.
23	叶健, 葛临东, 吴月娴. 一种优化的RBF神经网络在调制识别中的应用[J]. 自动化学报, 2007, 33(6): 652-654.
	YE Jian, GE Lindong, WU Yuexian. An application of improved RBF neural network in modulation recognition[J]. Acta Automatica Sinica, 2007, 33(6): 652-654.
24	乔俊飞, 韩红桂. RBF神经网络的结构动态优化设计[J]. 自动化学报, 2010, 36(6): 865-872.
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[J]. Flow Measurement and Instrumentation, 2020, 76: 101834.
2	罗小明，何利民，吕宇玲. 油气水三相段塞流与滚动波流型的转变[J]. 化工学报，2010，61(3)：629-634.
	LUO Xiaming, HE Limin, Yuling LYU. Transition from slug flow to roll wave flow in oil-gas-water three-phase horizontal pipe[J]. CIESC Journal, 2010, 61(3): 629-634.
3	MANDHANE J M, GREGORY G A, AZIZ K. A flow pattern map for gas-liquid flow in horizontal pipes[J]. International Journal of Multiphase Flow, 1974, 1(4): 537-553.
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[J]. SPE Production & Operations, 2013, 28(3): 306-316.
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[J]. Flow Measurement and Instrumentation, 2016, 50: 112-120.
6	GUILIZZONI Manfredo, BACCINI Beatrice, SOTGIA Giorgio, et al. Image-based analysis of intermittent three-phase flow[J]. International Journal of Multiphase Flow, 2018, 107: 256-262.
7	MUKHERJEE Tanumoy, Gargi DAS, Subhabrata RAY. Sensor-based flow pattern detection—Gas-liquid-liquid upflow through a vertical pipe[J]. AIChE Journal, 2014, 60(9): 3362-3375.
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[J]. Experiments in Fluids, 2005, 39(4): 667-678.
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[C]//17th International Conference on Multiphase Production Technology, Cannes, France, 2015: 255-267.
10	HOFFMANN Rainer, JOHNSON George William. Measuring phase distribution in high pressure three-phase flow using gamma densitometry[J]. Flow Measurement and Instrumentation, 2011, 22(5): 351-359.
11	FALCONE Gioia, HEWITT Geoffrey, ALIMONTI Claudio. Multiphase flow metering principles[J]. Developments in Petroleum Science, 2009, 54: 33-45.
12	REN Weikai, ZHAO An, JIN Ningde. Void fraction measurement of oil-gas-water three-phase flow using mutually perpendicular ultrasonic sensor[J]. Sensors, 2020, 20(2): 481.
13	YEN Gary G, LU Haiming. Acoustic emission data assisted process monitoring[J]. ISA Transactions, 2002, 41(3): 273-282.
14	BROOMHEAD D, LOWE D. Radial basis functions, multi-variable functional interpolation and adaptive networks[R]. RSRE, 1988
24	QIAO Junfei, HAN Honggui. Optimal structure design for RBFNN structure[J]. Acta Automatica Sinica, 2010, 36(6): 865-872.

网络类型	样本数	识别数	识别率/%
BP网络	140	129	92.1
深层神经网络	140	133	95.0
RBF网络	140	134	95.7

网络类型	样本数	识别数	识别率/%
BP网络	140	129	92.1
深层神经网络	140	133	95.0
RBF网络	140	134	95.7

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