Oil-Gas-Water Three-Phase Flow Pattern Identification Through Parallel Decision Trees With Differential Pressure and Ultrasonic Sensors - Shi, Xuewei; Tan, Chao; Dong, Feng (IEEE transactions on instrumentation and measurement, 2024)
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.