Flow Pattern and Oil Holdup Prediction in Vertical Oil–Water Two–Phase Flow Using Pressure Fluctuation Signal

Document Type: Research Article

Authors

Department of Chemical Engineering, Yasouj University, P.O. Box 75914-353 Yasouj, I.R. IRAN

Abstract

In this work, the feasibility of flow pattern and oil hold up the prediction for vertical upward oil–water two–phase flow using pressure fluctuation signals was experimentally investigated. Water and diesel fuel were selected as immiscible liquids. Oil hold up was measured by Quick Closing Valve (QCV) technique, and five flow patterns were identified using high-speed photography through a transparent test section with Inner Diameter (ID) of 0.0254 m. The observed flow patterns were Dispersed Oil in Water (D O/W), Dispersed Water in Oil (D W/O), Transition Flow (TF), Very FineDispersed Oil in Water (VFD O/W) and a new flow pattern called Dispersed Oil Slug & Water in Water (D OS& W/W). The pressure fluctuation signals were also measured by a static pressure sensor and decomposed at five levels using wavelet transform. Then, standard deviation values of decomposition levels were used as input parameters of a Probabilistic Neural Network (PNN) to train the network for predicting the flow patterns. In addition, some considered numerical values for actual flow patterns together with the signal energy value of each level were used as input parameters of a MultiLayer Perceptron (MLP) network to estimate the oil holdup. The results indicated good accuracy for recognition of the flow patterns (accuracy of 100% and 95.8% for training data and testing data, respectively) and oil holdup (AAPE=9.6%, R=0.984 for training data and AAPE=8.07%, R=0.99 for testing data).

Keywords

Main Subjects


[1] Azizi S., Awad M.M., Ahmadloo E., Prediction of Water Holdup in Vertical and Inclined Oil–Water Two–Phase Flow Using Artificial Neural Network, Int. J. Multiphase Flow, 80: 181–187 (2016).

[2] Govier G.W., Sullivan G.A., Wood R.K., The Upward Vertical Flow of Oil–Water Mixtures, Can. J. Chem. Eng., 4: 67–75 (1961).

[3] Flores J.G., Chen X.T., Sarica C., Brill J.P., Characterization of Oil–Water Flow Patterns in Vertical and Deviated Wells, SPE. Prod. Facil., 14: 102–109 (1999).

[4] Jana K., Das G., Das P.K., Flow Regime Identification of Two–Phase Liquid–Liquid Upflow Through Vertical Pipe, Chem. Eng. Sci., 61: 1500–1515 (2006).

[5] Du M., Jin N.D., Gao Z.K, Wang Z.Y., Zhai L.S., Flow Pattern and Water Holdup Measurements of Vertical Upward Oil–Water Two–Phase Flow in Small Diameter Pipes, Int. J. Multiphase Flow, 41: 91–105 (2012).

[6] Mydlarz–Gabryk K., Pietrzak M., Troniewski L., Study on Oil–Water Two–Phase Upflow in Vertical Pipes, J. Petrol. Sci. Eng., 117: 28–36 (2014).

[7] Parker D.J., McNeil P.A., Positron Emission Tomography for Process Applications, Meas. Sci. Technol., 7: 287–296 (1996).

[8] Bemrose C.R., Fowles P., Hawkesworth M.R., O’Dwyer M.A., Application of Positron Emission Tomography to Particulate Flow Measurement in Chemical Engineering Processes, Nuclear. Instruments. Meth. A., 273: 874–880 (1988).

[9] Mantle M.D., Sederman A.J., Dynamic MRI in Chemical Process and Reaction Engineering, Prog. Nucl. Mag. Res. Sp., 43(1): 3–60 (2003).

[10] Holland D.J., Müller C.R., Dennis J.S., Gladden L.F., Davidson J.F., Magnetic Resonance Studies of  Fluidization Regimes, Ind. Eng. Chem. Res., 49. 5891–5899 (2010).

[11] Reyes Jr. J.N., Lafi A.Y., Saloner D., The Use of MRI to Quantify Multiphase Flow Patterns and Transitions: An Application to Horizontal Slug Flow, Nucl. Eng. Des., 184: 213–228 (1998).

[12] Bieberle M., Fischer F., Schleicher E., Hampel U., Koch D., Aktay K.S.C., Menz H.J., Mayer H.G., Ultrafast Limited–Angle–Type X–Ray Tomography, Appl. Phys. Lett., 91: 123516 (2007).

[13] Kumar S.B., Moslemian D., Duduković M., A γ–Ray Tomographic Scanner for Imaging Voidage Distribution in Two–Phase Flow Systems, Flow. Meas. Instrum., 6(1): 61–73 (1995).

[14] Yang M., Schlaberg H.I., Hoyle B.S., Beck M.S., Lenn C., Real–Time Ultrasound Process Tomography for Two–Phase Flow Imaging Using a Reduced Number of Tansducers IEEE Transactions on Ultrasonics, Ferr. Freq. Control., 46: 492–501 (1999).

[16] Geraets J.J.M., Borst J.C., A Capacitance Sensor for Two–Phase Void Fraction Measurement and Flow Pattern Identification, Int. J. Multiphase Flow, 14: 305–320 (1988).

[17] Xie C.G., Reinecke N., Beck M.S, Mewes D., Williams R.A., Electrical Tomography Techniques for Process Engineering Applications, Chem. Eng. J., 56: 127–133 (1995).

[18] Prasser H.M., Böttger A., Zschau J., A New Electrode–Mesh Tomograph for Gas–Liquid Flows, Flow. Meas. Instrum., 9: 111–119 (1998).

[19] Liu W., Tan C., Dong F., A Wire–Mesh Sensor for Air–Water Two–Phase Flow Imaging, IEEE “International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings”, 364–369 (2015).

[20] Smith A.V., Transient Density Measurements in Two–Phase Flows Using X–Rays, J. Br. Nucl. Energy Soc., 10: 99–106 (1971).

[21] Eberle C.S., Lenug W.H., Ishii M., Revankar S.T., Optimization of a One–Shot Gamma Densitometer for Measuring Area–Averaged Void Fractions of Gas–Liquid Flows in Narrow Pipelines, Meas. Sci. Technol., 5: 1146–1158 (1994).

[22] Luggar R.D., Key M.J., Morton E.J., Gilboy W.B., Energy Dispersive X–Ray Scatter for Measurement of Oil–Water Ratios, Nucl. Instrum. Meth. A., 422: 938–941. (1999)

[23] Kumara W.A.S., Halvorsen B.M., Melaaen M.C., Single–Beam Gamma Densitometry Measurements of Oil–Water Flow in Horizontal and Slightly Inclined Pipes, Int. J. Multiphase Flow, 36: 467–480 (2010).

[24] Da Silva M.J., Schleicher E., Hampel U., Capacitance Wire–Mesh Sensor for Fast Measurement of Phase Fraction Distributions, Meas. Sci. Technol., 18: 2245–51 (2007).

[25] Shaban, H.; Tavoularis, S, The Wire–Mesh Sensor as a Two–Phase Flow Meter, Meas. Sci. Technol., 26: 015306 (2015). 

[26] Zhii L.S., Jin N.D., Gao Z.K., Wang Z.Y., Li D.M., The Ultrasonic Measurement of High Water Volume Fraction in Dispersed Oil–in–Water Flows, Chem. Eng. Sci., 94: 271–283 (2013).

[27] Tsouris C., Norato M.A., Tavlarides L.L., A Pulse–Echo Ultrasonic Probe for Local Volume Fraction Measurements in Liquid–Liquid Dispersions, Ind. Eng. Chem. Res., 34: 3154–3158 (1995).

[28] Strizzolo C., Converti J., Capacitance Sensors for Measurement of Phase Volume Fraction in Two–Phase Pipelines, IEEE. Trans. Instrum. Meas., 42: 726–729 (1993).

[30] Huang S.F., Zhang X.G., Wang D., Lin Z.H., Water Holdup Measurement in Kerosene–Water Two–Phase Flows, Meas. Sci. Technol., 18: 3784–3794 (2007).

[31] Demoria M., Ferrari V., Strazza D., Poesio P., Capacitive Sensor System for the Analysis of Two–Phase Flows of Oil and Conductive Water, Sensors. Actuat. A., 163: 172–179 (2010).

[32] Strazza D., Demori M., Ferrari V., Poesio P., Capacitance Sensor for Hold–up Measurement in High–Viscous–Oil/Conductive–Water Core–Annular Flows, Flow. Meas. Instrum., 22: 360–369 (2011).

[33] Sardeshpande M.V., Harinarayan S., Ranade V.V., Void Fraction Measurement Using Electrical Capacitance Tomography and High Speed Photography, Chem. Eng. Res. Des., 94: 1–11 (2015).

[34] Shang Z., Yang R., Gao X., Yang Y., An Investigation of Two–Phase Flow Instability Using Wavelet Signal Extraction Technique, Nucl. Eng. Des., 232(2): 157–163 (2004).

[36] Chakrabarti D.P., Das G., Das P.K., Identification of Stratified Liquid–Liquid Flow Through Horizontal Pipes by a Non–Intrusive Optical Probe, Chem. Eng. Sci., 62: 1861–1876 (2007).

[38] Nguyen V.T., Euh D.J., Song C.H., An Application of the Wavelet Analysis Technique for the Objective Discrimination of Two–Phase Flow Patterns, Int. J. Multiphase Flow, 36: 755–768 (2010).

[40] Drahos J., Zahradnik J., Puncochar M., Fialova M., Bradka F., Effect of Operating Conditions on the Characteristics of Pressure Fluctuations in a Bubble Column, Chem. Eng. Process., 29: 107–115 (1991).

[41] Rosa E.S., Salgado R.M., Ohishi T., Mastelari N., Performance comparison of Artificial Neural Networks and Expert Systems Applied to Flow Pattern Identification in Vertical Ascendant Gas–Liquid Flows, Int. J. Multiphase Flow, 36: 738–754 (2010).

[42] Han Y.F., Zhao A., Zhang H.X., Ren Y.Y., Liu W.X., Jin N.D., Differential Pressure Method for Measuring Water Holdup of Oil–Water Two–Phase Flow with Low Velocity and High Water–Cut, Exp. Therm. Fluid Sci., 72: 197–209 (2016).

[43] Drahos J., Cermak J., Diagnostics of Gas–Liquid Flow Patterns in Chemical–Engineering Systems, Chem. Eng. Process., 26: 147–164 (1989).

[44] Park S.H., Kang Y., Kim S.D., Wavelet Transform Analysis of Pressure Fluctuation Signals in a Pressurized Bubble Column, Chem. Eng. Sci., 56: 6259–6265 (2001).

[47] Elperin T., Klochko M., Flow Regime Identification in a Two–Phase flow using wavelet transform, Exp. Fluids., 32: 674–682 (2002).

[49] Brauner N., Moalem Maron D., Stability Analysis of Stratified Liquid–Liquid Flow, Int. J. Multiphase Flow, 18: (1992) 103–121.

[50] Marseguerra M., Minoggio S., Rossi A., Zlo E., Artificial Neural Networks Applied to Multiple Signals in Nuclear Technology, Prog. Nucl. Energy, 27(4): 297–304 (1992).

[51] Cai S., Toral H., Qiu J., Archer J.S., Neural Network Based Objective Flow Regime Identification in Air–Water Two–Phase Flow, Can. J. Chem. Eng., 72: 440–445 (1994).

[52] Antonopoulosdomis M., Tambouratzis T., Artificial Neural Networks for Neutron Source Localization Within Sealed Tnks, Ann. Nucl. Energy, 23(18): 1477–1488 (1996).

[53] Xie T., Ghiasasiaan S.M., Karrila S., Flow Regime Identification in Gas–Liquid–Pulp Fiber Flow Based on Pressure Fluctuations Using ANN, Ind. Eng. Chem. Res., 42: 7014–7024 (2003).

[54] Peng Z., Yin H., ECT and LS–SVM Based Void Fraction Measurement of Oil–Gas Two–Phase Flow, Iran. J. Chem. Chem. Eng. (IJCCE), 29(1): 41–50 (2010).

[55] Salgado C.M., Pereira C.M.N.A., Schirru R., Brandão L.E.B., Flow Regime Identification and Volume Fraction Prediction in Multiphase Flows by Means of Gamma–Ray Attenuation and Artificial Neural Networks, Prog. Nucl. Energy, 52: 555–562 (2010).

[57] Bin S., Hong W., Identification Method of Gas–Liquid Two–Phase Flow Regime Based on Wavelet Packet Energy Feature and PNN, International Conference on ICCE2011, AISC, 112: 595–603 (2011).

[58] Nazemi E., Feghhi S.A.H., Roshani G.H., Gholipour Peyvandi R., Setayeshi S., Precise Void Fraction Measurement in Two–Phase Flows Independent of the Flow Regime Using Gamma–Ray Attenuation, Nucl. Eng. Technol., 48: 64–71 (2015).

[59] Cong T., Su G., Qiu S., Tian W., Applications of ANNs in Flow and Heat Transfer Problems in Nuclear Engineering: A Review Work, Prog. Nucl. Energy, 62: 54–71(2013).

[60] Daubechies I., The Wavelet Transform, time–Frequency Localization and Signal Analysis, IEEE. T. Inform. Theory, 36: 961–1005 (1990).

[61] Daubechies I., "Ten lectures on Wavelets. Society for Industrial and Applied Mathematics", SIAM Publication. Philadelphia. (1992).

[62] Jana K., Das G., Das P.K., The hydrodynamics of Liquid–Liquid Upflow Through a Venturimeter, Int. J. Multiphase Flow, 34: 1119–1129 (2008).

[63] Jafari, M.R., Salahshoor K., Adaptive Predictive Controllers Using a Growing and Pruning RBF Neural Network, Iran. J. Chem. Chem. Eng. (IJCCE), 30(2): 125–138 (2011).

[64] Azari A, Shariaty–Niassar M, Short–Term and Medium–Term Gas Demand Load Forecasting by Neural Networks, Iran. J. Chem. Chem. Eng. (IJCCE), 31(4): 77–84 (2012).

[65] Karimi H., Yousefi F., Rahimi M.R., Correlation of Viscosity in Nanofluids Using Geneticalgorithm–Neural Network (GA–NN), Heat. Mass. Transfer. J., 11: 1417–1425 (2011).

[66] Timung S., Mandal T.K., Prediction of Flow Pattern of Gas–Liquid Flow Through Circular Microchannel Using Probabilistic Neural Network, App. Soft. Comput., 13: 1674–1685 (2013).

[67] Specht D. F., Probabilistic Neural Networks, Neural Networks, 3: 109–118 (1990).

[68] Sayyad H., Manshad A.K., Rostami H., Application of Hybrid Neural Particle Swarm Optimization Algorithm for Prediction of MMP, Fuel, 116: 625–633 (2014).

[69] Bulsari A.B., "Neural Networks for Chemical Engineers", Amsterdam, Elsevier, (1996).

[71] Terrence L.F., "Feedforward Neural Network Methodology", New York, Springer, (1999).