scholarly journals Particle Image Velocimetry of Oil–Water Two-Phase Flow with High Water Cut and Low Flow Velocity in a Horizontal Small-Diameter Pipe

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2702 ◽  
Author(s):  
Lianfu Han ◽  
Haixia Wang ◽  
Xingbin Liu ◽  
Ronghua Xie ◽  
Haiwei Mu ◽  
...  

Velocity and flow field are both parameters to measure flow characteristics, which can help determine the logging location and response time of logging instruments. Particle image velocimetry (PIV) is an intuitive velocity measurement method. However, due to the limitations of image acquisition equipment and the flow pipe environment, the velocity of a horizontal small-diameter pipe with high water cut and low flow velocity based on PIV has measurement errors in excess of 20%. To solve this problem, this paper expands one-dimensional displacement sub-pixel fitting to two dimensions and improves the PIV algorithm by Kriging interpolation. The improved algorithm is used to correct the blank and error vectors. The simulation shows that the number of blank and error vectors is reduced, and the flow field curves are smooth and closer to the actual flow field. The experiment shows that the improved algorithm has a maximum measurement error of 5.9%, which is much lower than that of PIV, and that it also has high stability and a repeatability of 3.14%. The improved algorithm can compensate for the local missing flow field and reduce the requirements related to the measurement equipment and environment. The findings of this study can be helpful for the interpretation of well logging data and the design of well logging instruments.

Author(s):  
K.I. Ojukwu ◽  
M.I. Khalil ◽  
J. Clark ◽  
H. Sharji ◽  
J. Edwards ◽  
...  

2007 ◽  
Author(s):  
Kelechi Isaac Ojukwu ◽  
John Ernest Edwards ◽  
Mosleh Mohamed Khalil ◽  
James Edward Clark ◽  
Hamed Hamoud Al-Sharji ◽  
...  

1999 ◽  
Author(s):  
Javier Ortiz-Villafuerte ◽  
William D. Schmidl ◽  
Yassin A. Hassan

Abstract The particle image velocimetry measurement technique was used to measure the whole-volume, three-dimensional, transient velocity field generated by a single air bubble rising in stagnant water in a small diameter pipe. The three-dimensional flow field was reconstructed using a stereoscopic technique. Conditional averages of the velocity fields for the situations when the bubble rises close to the center of the pipe, and close to the pipe wall were determined, and the turbulent motion generated in the continuous liquid phase for both situations was studied.


2019 ◽  
Vol 16 (3) ◽  
pp. 302-313 ◽  
Author(s):  
Da-Yang Wang ◽  
Ning-De Jin ◽  
Lu-Sheng Zhai ◽  
Ying-Yu Ren ◽  
Yuan-Sheng He

2020 ◽  
Vol 20 (2) ◽  
pp. 93-103
Author(s):  
Lianfu Han ◽  
Haixia Wang ◽  
Yao Cong ◽  
Xingbin Liu ◽  
Jian Han ◽  
...  

AbstractVelocity 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.


2007 ◽  
Author(s):  
Kelechi Isaac Ojukwu ◽  
John Ernest Edwards ◽  
Mosleh Mohamed Khalil ◽  
James Edward Clark ◽  
Hamed Hamoud Al-Sharji ◽  
...  

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