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Proceedings Paper

A two-phase flow meter targeting high GVF
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Paper Abstract

Measuring in real-time two phase flow composition of a mixed fluid having high gas void fraction (GVF) remains a challenging task in oil-gas fields. Such fluid is abundant in gas pipelines where pressure and temperature fluctuations lead to condensate gas. This may also be the case of crude oil produced from CO2 or steambased enhanced oil recovery (EOR); where the injected gas is mixed with the produced oil. This paper presents a new concept of high GVF measurement using a Terahertz-based imaging system. It explores the fact the gas phase has very low absorption of THz waves, while it yields an absorption factor that is proportional to the amount of liquid. The recent availability of low cost THz imaging systems that can generate two dimensional (2D) images at more than 100 frames/seconds make them well suitable for flow metering applications. Two different Artificial Intelligence (AI) algorithms, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN), were assessed using an inhouse multiphase flow loop. The corresponding results reveal that while ANN and SVM yield very accurate results, the SVM technique performed slightly better where a maximal error of 0.46 % for GVF in the GVF range from 80 to 100% could be achieved. This suggests that the technique can be considered as a good candidate for next generation flow metering and imaging of multiphase flows.

Paper Details

Date Published: 6 September 2019
PDF: 17 pages
Proc. SPIE 11124, Terahertz Emitters, Receivers, and Applications X, 1112416 (6 September 2019); doi: 10.1117/12.2536988
Show Author Affiliations
Qasim Al Bulooshi, Khalifa Univ. of Science, Technology and Research (United Arab Emirates)
Mahmoud Al Shehi, Khalifa Univ. of Science, Technology and Research (United Arab Emirates)
Abdelaziz Al Amri, Khalifa Univ. of Science, Technology and Research (United Arab Emirates)

Published in SPIE Proceedings Vol. 11124:
Terahertz Emitters, Receivers, and Applications X
Manijeh Razeghi; Alexei N. Baranov; Miriam S. Vitiello, Editor(s)

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