An Improved Particle Image Velocimetry Algorithm for Velocity Measurement of Oil-Water Two-Phase Flow

Kong, Ling Fu; Kong, Wei Hang; Li, Ying Wei; Zhang, Cong; Du, Sheng Xu

doi:10.4028/www.scientific.net/amm.602-605.1654

Cited by 4 publications

(3 citation statements)

References 21 publications

(35 reference statements)

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“…Balusamy et al [11] presented a new experimental approach using PIV technique to measure the local instantaneous laminar burning velocity of a stretched premixed flame and developed a new algorithm based on adaptive interrogation window scheme by taking into account the flow and flame front topologies to achieve the required spatial resolution. Kong et al [12] proposed an improved PIV algorithm which can reduce the false vectors for the velocity field of oil-water two-phase dispersed flow in horizontal pipe. Timmins et al [13] developed a code to measure each value of motion parameters and estimate the velocity uncertainty due to the PIV algorithm for each vector in the flow field.…”

Section: Introductionmentioning

confidence: 99%

Phase discrimination and a high accuracy algorithm for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump

Shi

Wei

Zhang

2015

Flow Measurement and Instrumentation

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Section: Introductionmentioning

confidence: 99%

Phase discrimination and a high accuracy algorithm for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump

Shi

Wei

Zhang

2015

Flow Measurement and Instrumentation

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“…The experiment results show that the improved algorithm has strong robustness under complex conditions such as image blurring and noise interference. In the velocity measurement of oil-water two-phase flow, Kong et al proposed a PTV algorithm based on the scaleinvariant feature transform (SIFT) feature point matching, which was applied to velocity measurement of oil-water two-phase flow with low particle concentration [19]. The measurement results are of high precision and the method is applicable to oil droplet overlap and oil droplet size distribution.…”

Section: Introductionmentioning

confidence: 99%

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Han

Wang

Yao

et al. 2020

Measurement Science Review

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Velocity is an important parameter for fluid flow characteristics in profile logging. Particle tracking velocimetry (PTV) technology is often used to study the flow characteristics of oil wells with low flow velocity and high water cut, and the key to PTV technology is particle matching. The existing particle matching algorithms of PTV technology do not meet the matching demands of oil drops in the oil phase velocity measurement of oil-water two-phase flow with low velocity and high water cut. To raise the particle matching precision, we improved the particle matching algorithm from the oriented FAST and the rotated BRIEF (ORB) feature description and the random sample consensus (RANSAC) algorithm. The simulation and experiment were carried out. Simulation results show that the improved algorithm not only increases the number of matching points but also reduces the computation. The experiment shows that the improved algorithm in this paper not only reduces the computation of the feature description process, reaching half of the computation amount of the original algorithm, but also increases the number of matching results, thus improving the measurement accuracy of oil phase velocity. Compared with the SIFT algorithm and the ORB algorithm, the improved algorithm has the largest number of matching point pairs. And the variation coefficient of this algorithm is 0.039, which indicates that the algorithm is stable. The mean error of oil phase velocity measurement of the improved algorithm is 1.20 %, and the maximum error is 6.16 %, which is much lower than the maximum error of PTV, which is 25.89 %. The improved algorithm overcomes the high computation cost of the SIFT algorithm and achieves the precision of the SIFT algorithm. Therefore, this study contributes to the improvement of the measurement accuracy of oil phase velocity and provides reliable production logging data for oilfield.

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“…Xu et al [33] applied DPIV to the small diameter horizontal oil-water two-phase immiscible flow with oil droplets as tracer particles, and the results represented the average accumulation flow velocity of oil droplets in the shooting direction. Kong et al [34] applied DPIV to oil-water two-phase immiscible flow in a small diameter horizontal well, and modified the window overlap method in the WIDIM algorithm to improve the measurement accuracy. Although these algorithms are effective in their application filed, they do not perform well for oil-water two-phase immiscible flow measurement in an inner diameter 125 mm vertical upward pipe.…”

Section: Introductionmentioning

confidence: 99%

Flow Velocity Field Measurement of Vertical Upward Oil–Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS

et al. 2019

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Flow velocity field measurement is important for analyzing flow characteristics of oil-water two-phase immiscible flow in vertical well. Digital particle image velocimetry (DPIV) is an effective velocity field measurement method that has overcome single point measurement limitation of traditional instruments. However, multiphase flow velocity fields generated by DPIV are often accompanied by local false vectors caused by image mismatching, which leads to measurement results with low accuracy. In this paper, the reasons for oil-water two-phase immiscible flow image mismatching in inner diameter 125 mm vertical pipe is identified by studying the DPIV calculation process. This is mainly caused by image noise and poor window following performance that results from poor deformation performance of the interrogation window. To improve deformation performance of the interrogation window, and thus improve the accuracy of the algorithm, iterative closest point (ICP) and moving least squares (MLS) are introduced into the window deformation iterative multigrid algorithm in DPIV postprocessing algorithm. The simulation showed that the improved DPIV algorithm had good matching performance, and thus the false vector was reduced. The experimental results showed that, in light of the present investigation, on average, the improved DPIV algorithm is found to yield an accuracy improvement of~6%; the measurement uncertainty and reproducibility of the improved DPIV algorithm were 0.149 × 10 −3 m/s and 1.98%, respectively.

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An Improved Particle Image Velocimetry Algorithm for Velocity Measurement of Oil-Water Two-Phase Flow

Cited by 4 publications

References 21 publications

Phase discrimination and a high accuracy algorithm for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump

Phase discrimination and a high accuracy algorithm for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Flow Velocity Field Measurement of Vertical Upward Oil–Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS

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