Methodology for production logging in oil-in-water flows under low flow rate and high water-cut conditions

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

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

2019 ◽  
Vol 289 ◽  
pp. 165-179
Author(s):  
Jing Ma ◽  
Ning-De Jin ◽  
Da-Yang Wang ◽  
Dong-Yang Liu ◽  
Wei-Xin Liu

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.


Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4649 ◽  
Author(s):  
Jin ◽  
Zhou ◽  
Liang ◽  
Wang ◽  
Zhai ◽  
...  

In order to improve the flow measurement accuracy of oil-water two-phase flow at low flow rate, this paper presents a plug-in conductance sensor array (PICSA) for the measurement of water holdup and cross-correlation velocity. Due to the existence of the insert body in PICSA, the effect of slippage and the non-uniform distribution of dispersed phase on the measurement of oil-water two-phase flow at low flow rate can be reduced. The finite element method is used to analyze the electric field distribution characteristics of the plug-in conductance sensor, and the sensor geometry is optimized. The dynamic experiment of oil-water two-phase flow is carried out where water cut Kw and mixture velocity Um are set in the range of 10–98% and 0.0184–0.2580 m/s respectively. Experimental results show that the PICSA has good resolution in water holdup measurement for dispersed oil-in-water slug flow (D OS/W), transition flow (TF), dispersed oil-in-water bubble flow (D O/W) and very fine dispersed oil-in-water bubble flow (VFD O/W). In addition, the cross-correlation velocity of the oil-water two-phase flow is obtained by using the plug-in upstream and downstream conductance sensor arrays. The relationship between the cross-correlation velocity and mixture velocity is found to be sensitive to the change of flow pattern, but it has a good linear relationship under the same flow pattern. Based on the flow pattern identification, a good prediction result of the mixture velocity is obtained using kinematic wave theory. Finally, a high precision prediction of the individual phase volume fraction of oil-water two-phase flow at low flow rate is achieved by using the drift flux model.


2021 ◽  
pp. 1-1
Author(s):  
Landi Bai ◽  
Ningde Jin ◽  
Xin Chen ◽  
Lusheng Zhai ◽  
Jidong Wei ◽  
...  

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