Oil-Gas-Water Three-Phase Flow Pattern Identification Through Parallel Decision Trees With Differential Pressure and Ultrasonic Sensors

A deep understanding of oil-gas-water three-phase flow behaviors and mechanics has great significance for flow controlling and modeling, production efficiency improvement, and operation safety assurance. To accurately identify horizontal oil-gas-water three-phase flow patterns, a decision-level fusi...

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Published in	IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 15
Main Authors	Shi, Xuewei, Tan, Chao, Dong, Feng
Format	Journal Article
Language	English
Published	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects	Acoustics Cost analysis Decision tree Decision trees Differential pressure differential pressure (DP) sensor Feature extraction Flow characteristics Flow distribution flow pattern identification Flow velocity Fluids Frequency measurement Intelligibility oil–gas–water three-phase flow Performance evaluation Pressure measurement pulse wave ultrasonic Doppler (PWUD) sensor Sensor phenomena and characterization Sensors Ultrasonic imaging Ultrasonic variables measurement Velocity distribution
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ISSN	0018-9456 1557-9662
DOI	10.1109/TIM.2024.3470252

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Abstract	A deep understanding of oil-gas-water three-phase flow behaviors and mechanics has great significance for flow controlling and modeling, production efficiency improvement, and operation safety assurance. To accurately identify horizontal oil-gas-water three-phase flow patterns, a decision-level fusion method with a parallel-tree structure is proposed based on a combined differential pressure (DP) and pulse wave ultrasonic Doppler (PWUD) sensor. With advantages of nonintrusive, easy-to-mount operation, and low cost, the combined sensor can simultaneously acquire various flowing information, including instantaneous DP fluctuations, time-varying echograms, and velocity profiles. By analyzing the dynamic sensor responses to different flow behaviors, typical flow characteristics of oil-gas-water three-phase flow are first revealed. On this basis, several features are extracted from the time-accumulated echograms, average flow velocity time series, and decomposed DP fluctuations to objectively characterize the flow patterns from different perspectives in the time, space, and time-frequency domains. Then, two parallel decision trees are constructed to, respectively, identify the gas-liquid interphase structure and oil-water interphase structure. Using the visibility of tree structures, the ability of these extracted features for flow structure distinguishment is analyzed. Finally, the final decision on the oil-gas-water three-phase flow pattern was generated by fusion the outputs of two decision trees. Tenfold cross-validation method is adopted for model performance evaluation. The results show that 12 types of oil-gas-water three-phase flow pattern are identified with the overall identification accuracy of 92.1%. This study provides a simple, affordable, and nonintrusive solution with good intelligibility and interpretability for flow pattern identification of complex industrial three-phase flows.
AbstractList	A deep understanding of oil–gas–water three-phase flow behaviors and mechanics has great significance for flow controlling and modeling, production efficiency improvement, and operation safety assurance. To accurately identify horizontal oil–gas–water three-phase flow patterns, a decision-level fusion method with a parallel-tree structure is proposed based on a combined differential pressure (DP) and pulse wave ultrasonic Doppler (PWUD) sensor. With advantages of nonintrusive, easy-to-mount operation, and low cost, the combined sensor can simultaneously acquire various flowing information, including instantaneous DP fluctuations, time-varying echograms, and velocity profiles. By analyzing the dynamic sensor responses to different flow behaviors, typical flow characteristics of oil–gas–water three-phase flow are first revealed. On this basis, several features are extracted from the time-accumulated echograms, average flow velocity time series, and decomposed DP fluctuations to objectively characterize the flow patterns from different perspectives in the time, space, and time-frequency domains. Then, two parallel decision trees are constructed to, respectively, identify the gas-liquid interphase structure and oil-water interphase structure. Using the visibility of tree structures, the ability of these extracted features for flow structure distinguishment is analyzed. Finally, the final decision on the oil–gas–water three-phase flow pattern was generated by fusion the outputs of two decision trees. Tenfold cross-validation method is adopted for model performance evaluation. The results show that 12 types of oil–gas–water three-phase flow pattern are identified with the overall identification accuracy of 92.1%. This study provides a simple, affordable, and nonintrusive solution with good intelligibility and interpretability for flow pattern identification of complex industrial three-phase flows.
Author	Tan, Chao Shi, Xuewei Dong, Feng
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SubjectTerms	Acoustics Cost analysis Decision tree Decision trees Differential pressure differential pressure (DP) sensor Feature extraction Flow characteristics Flow distribution flow pattern identification Flow velocity Fluids Frequency measurement Intelligibility oil–gas–water three-phase flow Performance evaluation Pressure measurement pulse wave ultrasonic Doppler (PWUD) sensor Sensor phenomena and characterization Sensors Ultrasonic imaging Ultrasonic variables measurement Velocity distribution
Title	Oil-Gas-Water Three-Phase Flow Pattern Identification Through Parallel Decision Trees With Differential Pressure and Ultrasonic Sensors
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