Open Access

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


Cite

[1] Saoud, A., Mosorov, V., Grudzien, K. (2017). Measurement of velocity of gas/solid swirl flow using Electrical Capacitance Tomography and cross correlation technique. Flow Measurement and Instrumentation, 53, 133-140.10.1016/j.flowmeasinst.2016.08.003Search in Google Scholar

[2] Dejam, M. (2019). Advective-diffusive-reactive solute transport due to non-Newtonian fluid flows in a fracture surrounded by a tight porous medium. International Journal of Heat and Mass Transfer, 128, 1307-1321.10.1016/j.ijheatmasstransfer.2018.09.061Search in Google Scholar

[3] Dejam, M., Hassanzadeh, H., Chen, Z. (2018). Semi-analytical solution for pressure transient analysis of a hydraulically fractured vertical well in a bounded dual-porosity reservoir. Journal of Hydrology, 565, 289-301.10.1016/j.jhydrol.2018.08.020Search in Google Scholar

[4] Papi, A., Sharifi, A., Abdali, M.R. (2019). Simulation of the effect of rock type on recovery plan of a mature carbonate oilfield in the Middle East–Part 1: Waterflooding recovery plan. Petroleum Science and Technology, 37 (11), 1251-1259.10.1080/10916466.2018.1550510Search in Google Scholar

[5] Gryzlov, A., Schiferli, W., Mudde, R.F. (2013). Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter. Flow Measurement and Instrumentation, 34, 91-104.10.1016/j.flowmeasinst.2013.09.002Search in Google Scholar

[6] Han, L., Hou, Y., Wang, Y., Liu, X., Han, J., Xie, R., Fu, C. (2019). Measurement of velocity of sand-containing Oil–Water two-phase flow with super high water holdup in horizontal small pipe based on thermal tracers. Flow Measurement and Instrumentation, 69, 101622.10.1016/j.flowmeasinst.2019.101622Search in Google Scholar

[7] Li, L., Kong, L., Xie, B., Fang, X., Kong, W., Liu, X., Zhao, F. (2019). The Influence on response of a combined capacitance sensor in horizontal oil–water two-phase flow. Applied Sciences, 9 (2), 346.10.3390/app9020346Search in Google Scholar

[8] Wei, J.D., Jin, N.D., Lian, E.Y., Wang, D., Han, Y., Zhai, L. (2018). Measurement of water holdup in oilin-water flows using three-channel conductance probe with center body. IEEE Sensors Journal, 18 (7), 2845-2852.10.1109/JSEN.2018.2804343Search in Google Scholar

[9] Xu, Z., Jiang, Y., Wang, B., Huang, Z., Ji, H., Li, H. (2017). Sensitivity distribution of CCERT sensor under different excitation patterns. IEEE Access, 5, 14830-14836.10.1109/ACCESS.2017.2713834Search in Google Scholar

[10] Liu, C., Xu, L., Chen, J., Cao, Z., Lin, Y., Cai, W. (2015). Development of a fan-beam TDLAS-based tomographic sensor for rapid imaging of temperature and gas concentration Optics Express, 23 (17), 22494-22511.10.1364/OE.23.02249426368217Search in Google Scholar

[11] Aguirre-Pablo, A.A., Aljedaani, A.B., Xiong, J., Idoughi, R., Heidrich, W., Thoroddsen, S.T. (2019). Single-camera 3D PTV using particle intensities and structured light. Experiments in Fluids, 60 (2), 25.10.1007/s00348-018-2660-7Search in Google Scholar

[12] Scharnowski, S., Bross, M., Kähler, C.J. (2019). Accurate turbulence level estimations using PIV/PTV. Experiments in Fluids, 60 (1).10.1007/s00348-018-2646-5Search in Google Scholar

[13] Fu, S., Biwole, P.H., Mathis, C. (2016). Numerical and experimental comparison of 3D Particle Tracking Velocimetry (PTV) and Particle Image Velocimetry (PIV) accuracy for indoor airflow study. Building and Environment, 100, 40-49.10.1016/j.buildenv.2016.02.002Search in Google Scholar

[14] Rubbert, A., Schröder, W. (2020). Iterative particle matching for three-dimensional particle-tracking velocimetry. Experiments in Fluids, 61 (2), 58.10.1007/s00348-020-2891-2Search in Google Scholar

[15] Gim, Y., Jang, D.K., Sohn, D.K., Kim, H., Ko, H.S. (2020). Three-dimensional particle tracking velocimetry using shallow neural network for real-time analysis. Experiments in Fluids, 61 (2), 1-8.10.1007/s00348-019-2861-8Search in Google Scholar

[16] Qin, Y., Zou, J., Tang, B., Wang, Y., Chen, H. (2020). Transient feature extraction by the improved orthogonal matching pursuit and K-SVD algorithm with adaptive transient dictionary. IEEE Transactions on Industrial Informatics, 16 (1), 215-227.10.1109/TII.2019.2909305Search in Google Scholar

[17] Manickam, A., Devarasan, E., Manogaran, G., Priyan, M.K., Varatharajan, R., Hsu, C.H., Krishnamoorthi, R. (2019). Score level based latent fingerprint enhancement and matching using SIFT feature. Multimedia Tools and Applications, 78 (3), 3065-3085.10.1007/s11042-018-5633-1Search in Google Scholar

[18] Ma, D., Lai, H.C. (2019). Remote sensing image matching based improved ORB in NSCT domain. Journal of the Indian Society of Remote Sensing, 47 (5), 801-807.10.1007/s12524-019-00958-ySearch in Google Scholar

[19] Kong, L.F., Kong, W.H., Li, Y.W., Zhang, C., Du, S.X. (2014). An improved particle image velocimetry algorithm for velocity measurement of oil-water two-phase flow. In Applied Mechanics and Materials. Trans Tech Publications Ltd., vol. 602, 1654-1659.Search in Google Scholar

[20] Yu, L., Yu, Z., Gong, Y. (2015). An improved ORB algorithm of extracting and matching. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8 (5), 117-126.Search in Google Scholar

[21] Han, L., Wang, H., Liu, X., Xie, R., Mu, H., Fu, C. (2019). Particle image velocimetry of oil-water two-phase flow with high Water cut and low flow velocity in a Horizontal small-diameter pipe. Sensors, 19, 2702.10.3390/s19122702663204431208105Search in Google Scholar

[22] Pang, Y., Li, A. (2019). An improved ORB feature point image matching method based on PSO. In Tenth International Conference on Graphics and Image Processing. SPIE, vol. 11069.10.1117/12.2524178Search in Google Scholar

[23] Shu, C.W., Xiao, X.Z. (2018). Orb-oriented mismatching feature points elimination. In 2018 IEEE International Conference on Progress in Informatics and Computing. IEEE, 246-249.10.1109/PIC.2018.8706272Search in Google Scholar

[24] Rublee, E., Rabaud, V., Konolige, K., Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In 2011 International Conference on Computer Vision. IEEE, 2564-2571.10.1109/ICCV.2011.6126544Search in Google Scholar

[25] Mohammad, S., Morris, T. (2017). Binary robust independent elementary feature features for texture segmentation. Advanced Science Letters, 23 (6), 5178-5182.10.1166/asl.2017.7336Search in Google Scholar

[26] Wang, R., Xia, Y., Wang, G., Tian, J. (2015). License plate localization in complex scenes based on oriented FAST and rotated BRIEF feature. Journal of Electronic Imaging, 24 (5), 053011.10.1117/1.JEI.24.5.053011Search in Google Scholar

[27] Bhat, A. (2017). Makeup invariant face recognition using features from accelerated segment test and eigen vectors. International Journal of Image and Graphics, 17 (1), 1750005.10.1142/S021946781750005XSearch in Google Scholar

[28] Xu, J., Chang, H. W., Yang, S., Wang, M. (2012). Fast feature-based video stabilization without accumulative global motion estimation. IEEE Transactions on Consumer Electronics, 58 (3), 993-999.10.1109/TCE.2012.6311347Search in Google Scholar

[29] Fotouhi, M., Hekmatian, H., Kashani-Nezhad, M.A., Kasaei, S. (2019). SC-RANSAC: Spatial consistency on RANSAC. Multimedia Tools and Applications, 78 (7), 9429-9461.10.1007/s11042-018-6475-6Search in Google Scholar

[30] Fischler, M.A., Bolles, R.C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24 (6), 381-395.10.1145/358669.358692Search in Google Scholar

[31] Mikolajczyk, K., Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (10), 1615-1630.10.1109/TPAMI.2005.18816237996Search in Google Scholar

[32] Choi, S., Kim, T., Yu, W. (1997). Performance evaluation of RANSAC family. Journal of Computer Vision, 24 (3), 271-300.Search in Google Scholar

[33] Nistér, D. (2005). Preemptive RANSAC for live structure and motion estimation. Machine Vision and Applications, 16 (5), 321-329.10.1007/s00138-005-0006-ySearch in Google Scholar

eISSN:
1335-8871
Language:
English
Publication timeframe:
6 times per year
Journal Subjects:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing