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Proceedings Paper

Application of artificial neural networks in oil and gas multiphase metering
Author(s): Huimin Yang; Yuxing Li; Fuxian Zhou; Jian Zhang; Shouping Dong
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Paper Abstract

In order to study the law of multiphase flow in pipeline and solve the on-line multiphase metering problem without separation of gas and liquid, a new type of multiphase flowmeter was developed and a series of water-gas two phase flows experiments in horizontal pipeline were carried out. And Artificial Neural Networks was used to process data after the experiments. The results show that Artificial Neural Networks could be used to simulate the relationship of the variables that were affected by many uncertain factors very well. And the relative error of liquid phase is less than 10% as well as the relative error of gaseous phase is less than 20%.

Paper Details

Date Published: 6 November 2006
PDF: 7 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63575D (6 November 2006); doi: 10.1117/12.717598
Show Author Affiliations
Huimin Yang, Univ. of Petroleum China (China)
Yuxing Li, Shengli Engineering and Consulting Co. Ltd. (China)
Fuxian Zhou, Univ. of Petroleum China (China)
Jian Zhang, Shengli Engineering and Consulting Co. Ltd. (China)
Shouping Dong, Univ. of Petroleum China (China)


Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence
Jiancheng Fang; Zhongyu Wang, Editor(s)

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