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An Efficient Method for Determining Capillary Pressure and Relative Permeability Curves from Spontaneous Imbibition Data
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In this paper, a very efficient method, called single matrix block analyzer (SMBA), has been developed to determine relative permeability and capillary pressure curves from spontaneous imbibition (SI) data. SMBA mimics realistically the SI tests by appropriate boundary conditions modeling. In the proposed method, a cuboid with an identical core plug height is considered. The equal dimensions of the cuboid in x and y directions are set such that the cylindrical core plug and the cuboid have the same shape factor. Thus, by avoiding the difficulties of the cylindrical coordinates, a representative model for the core plug is established. Appropriate grid numbers in xy and z directions are specified to the model. Furthermore, the rock and fluid properties of SI test are set in the SMBA. By supposing forms of the oilwater capillary pressure and relative permeability and comparing the oil recovery curves of SMBA and SI data, capillary pressure and relative permeability can be determined. The SMBA is demonstrated using three experimental data with different aging times. Suitable equations are employed to represent the capillary pressure and relative permeability curves. The genetic algorithm is used as the optimization tool. The obtained results, especially for capillary pressure, are in good agreement with the experimental data. Moreover, the Bayesian credible interval (P10 and P90) evaluated by the Neighborhood Bayes Algorithm (NAB) is quite satisfactory.
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1
17


Mojtaba
Ghaedi
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
Department of Chemical and Petroleum Engineering,
Iran
mojtaba.ghaedi@gmail.com


Zoltán E.
Heinemann
Montanuniversitaet Leoben, FranzJosefStrasse 18, 8700 Leoben, Austria
Montanuniversitaet Leoben, FranzJosefStrasse
Austria


Mohsen
Masihi
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
Department of Chemical and Petroleum Engineering,
Austria
masihi@eud.ir


Mohammad Hossein
Ghazanfari
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
Department of Chemical and Petroleum Engineering,
Iran
Spontaneous Imbibition
Single Matrix Block Analyzer
Recovery Curve
Genetic algorithm
Neighborhood Bayes Algorithm
[Anderson, W. G., Wettability Literature Survey Part 1: Rock/Oil/Brine Interactions and the Effects of Core Handling on Wettability, Journal of Petroleum Technology, Vol. 38, No. 10, p.11251144, 1986. ##Behbahani, H. S., Di Donato, G., and Blunt, M. J., Simulation of Countercurrent Imbibition in Waterwet Fractured Reservoirs, Journal of Petroleum Science and Engineering, Vol. 50, No. 1, p. 2139, 2006. ##Blair, P. M., Calculation of Oil Displacement by Countercurrent Water Imbibition, Society of Petroleum Engineers Journal, Vol., 4, No. 3, p.195202, 1964. ##Bourbiaux, B. and Kalaydjian, F., Experimental Study of Cocurrent and Countercurrent Flows in Natural Porous Media, SPE Reservoir Engineering, Vol. 5, No. 3, p. 361368. 1990. ##Buckley, S. E. and Leverett, M. C., Mechanism of Fluid Displacement in Sands, Transactions of the AIME, Vol. 146, No. 01, p. 107116, 1942. ##Chen, J., Miller, M. A., and Sepehrnoori, K., Theoretical Investigation of Countercurrent Imbibition in Fractured Reservoir Matrix Blocks, In Proceedings of SPE Reservoir Simulation Symposium, Society of Petroleum Engineers, 1995. ##Haugen, Å., Fernø, M., Mason, G., and Morrow, N. R., Capillary Pressure And relative Permeability Estimated from a Single Spontaneous Imbibition Test, Journal of Petroleum Science and Engineering, Vol. 115, p. 112, 2014. ##Heinemann, Z. E., Inhouse Research Technical Report, Leoben University, 2013. ##Honarpour, M., Koederitz, L., and Harvey, A. H., Relative Permeability of Petroleum Reservoirs, C.R.C. Press, Bahman, 1986. ##Hou, J., Luo, F.,Wang, D., Li, Z., and Bing, S., Estimation of the Wateroil Relative Permeability Curve from Radial Displacement Experiments Part 1: Numerical Inversion Method, Energy Fuels, Vol. 26, No. 7, p.42914299, 2012. ##Kashchiev, D. and Firoozabadi, A., Analytical Solutions for 1D Countercurrent Imbibition in Waterwet Media, SPE Journal, Vol. 8, No. 4, p.401408, 2003. ##Kazemi, H., Gilman, J. R., and Elsharkawy, A. M., Analytical and Numerical Solution of Oil Recovery from Fractured Reservoirs With Empirical Transfer Functions (includes Associated Papers 25528 and 25818), SPE Reservoir Engineering, Vol. 7, No. 2, p. 219227, 1992. ##Li, K., Yangtze, U., and Horne, R. N., Computation of Capillary Pressure and Global Mobility from Spontaneous Water Imbibition Into Oilsaturated Rock, SPE Journal, Vol. 10, No. 4, p. 458465, 2005. ##Ma, S., Morrow, N. R., and Zhang, X., Generalized Scaling of Spontaneous Imbibition Data for Strongly Waterwet Systems, Journal of Petroleum Science and Engineering, Vol. 18, No. 34, p. 165178, 1997. ##MATLAB OptimizationToolbox, Release 2013a, The MathWorks, Inc., Natick, Massachusetts, United States, 2013. ##Mattax, C. C. and Kyte, J. R., Imbibition Oil Recovery from Fractured, Waterdrive Reservoir, SPE Journal, Vol. 2, No. 2, p. 177184, 1962. ##MirzaeiPaiaman, A. and Masihi, M., Scaling Equations for Oil/Gas Recovery from Fractured Porous Media by CounterCurrent Spontaneous Imbibition: From Development to Application, Energy Fuels, Vol. 27, No. 8, p. 46624676, 2013. ##Pirker, B., Mittermeir, G., and Heinemann, Z., Numerically Derived Type Curves for Assessing Matrix Recovery Factors, In Proceedings of EUROPEC/EAGE Conference and Exhibition. Society of Petroleum Engineers, 2007. ##PooladiDarvish, M. and Firoozabadi, A., Cocurrent and Countercurrent Imbibition in a Waterwet Matrix Block, SPE Journal, Vol. 5, No. 1, p. 311, 2000. ##Pordel Shahri, M., Jamialahmadi, M., and Shadizadeh, S. R., New Normalization Index for Spontaneous Imbibition, Journal of Petroleum Science and Engineering, Vol. 82, p. 130139, 2012. ##Putra, E., Fidra, Y., New, I., and Schechter, D. Study of Waterflooding Process in Naturally Fractured Reservoirs from Static and Dynamic Iimbibition Experiments, In Nternational Symposium of the Society of Core Analysts. Colorado, 1999. ##Sambridge, M., Geophysical Inversion with a Neighbourhood AlgorithmII. Appraising the Ensemble, Geophysical Journal International, Vol. 138, No. 3, p. 727746, 1999. ##Schembre, J. M. and Kovscek, A. R., Estimation of Dynamic Relative Permeability and Capillary Pressure from Countercurrent Imbibition Experiments, Transport in Porous Media, Vol. 65, No. 1, p. 3151, 2006. ##Schmid, K. S. and Geiger, S., Universal Scaling of Spontaneous Imbibition for Arbitrary Petrophysical Properties: Waterwet and Mixedwet States and Handy’s Conjecture, Journal of Petroleum Science and Engineering, Vol. 101, p. 4461, 2013. ##Skjaeveland, S., Siqveland, L., Kjosavik, A., Thomas, W., and Virnovsky, G., Capillary Pressure Correlation for Mixedwet Reservoirs, SPE Reservoir Evaluation, Engineering, Vol. 3, No. 1, p. 6067, 2000. ##Zhang, X., Morrow, N., and Ma, S., Experimental Verification of a Modified Scaling Group for Spontaneous Imbibition, SPE Reservoir Engineering, Vol. 11, No., 4, p. 280285, 19##]
Toward a Thorough Approach to Predicting Klinkenberg Permeability in a Tight Gas Reservoir: A Comparative Study
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Klinkenberg permeability is an important parameter in tight gas reservoirs. There are conventional methods for determining it, but these methods depend on core permeability. Cores are few in number, but well logs are usually accessible for all wells and provide continuous information. In this regard, regression methods have been used to achieve reliable relations between log readings and Klinkenberg permeability. In this work, multiple linear regression, tree boost, general regression neural network, and support vector machines have been used to predict the Klinkenberg permeability of Mesaverde tight gas sandstones located in Washakie basin. The results show that all the four methods have the acceptable capability to predict Klinkenberg permeability, but support vector machine models exhibit better results. The errors of models were measured by calculating three error indexes, namely the correlation coefficient, the average absolute error, and the standard error of the mean. The analyses of errors show that support vector machine models perform better than the other models, but there are some exceptions. Support vector machine is a relatively new intelligence method with great capabilities in regression and classification tasks. Herein, support vector machine was used to predict the Klinkenberg permeability of a tight gas reservoir and the performances of four regression techniques were compared.
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36


Sadegh
Baziar
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Petroleum Engineering, Amirkabir
Iran


Mohammad Mobin
Gafoori
Persian Gulf Science and Technology Park, Bushehr, Iran
Persian Gulf Science and Technology Park,
Iran


Seyed Mehdi
Mohaimenian Pour
Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
Department of Mathematics and Computer Science,
Iran


Majid Nabi
Bidhendi
Institute of Geophysics, University of Tehran, Tehran, Iran
Institute of Geophysics, University of Tehran,
Iran
mnbhendi@ut.ac.ir


Reza
Hajiani
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Petroleum Engineering, Amirkabir
Iran
reza.hajiani@aut.ac.ir
Klinkenberg Permeability
Tight Gas Reservoir
Multiple Linear Regression
General Regression Neural Network
Support vector machine
[References ##Aguilera, R. and Harding, T., Stateoftheart Tight Gas Sands Characterization and Production Technology, Journal of Canadian Petroleum Technology, Vol. 47, p. 3741, 2008. ##AlAnazi, A. and Gates, I., Support Vector Regression for Porosity Prediction in a Heterogeneous Reservoir: a Comparative Study, Computers and Geosciences, Vol. 36, p. 14941503, 2010. ##AlAnazi, A. and Gates, I., Support Vector Regression to Predict Porosity and Permeability, Effect of Sample Size, Computers and Geosciences, Vol. 39, p. 6476, 2010. ##AlAnazi, A. F., Gates, I. D., and Azaiez, J., Innovative Datadriven Permeability Prediction in a Heterogeneous Reservoir, in EUROPEC/EAGE Conference and Exhibition, Society of Petroleum Engineers, 2009. ##AlBulushi, N., Araujo, M., and Kraaijveld, M., Predicting Water Saturation Using Artificial Neural Networks (ANNS), Neural Networks, Vol. 549, No. 57, p. 5762, 2007. ##Amari, S. I. and Wu, S., Improving Support Vector Machine Classifiers by Modifying Kernel Functions, Neural Networks, Vol. 12, No. 12, p. 783789, 1999. ##Aminian, K. and Ameri, S., Application of Artificial Neural Networks for Reservoir Characterization with Limited Data, Journal of Petroleum Science and Engineering, Vol. 3, No. 49, p. 212222, 2005. ##Aminian, K., Thomas, B., Bilgesu, H., Ameri, S., and Oyerokun, A., Permeability Distribution Prediction, in Proceeding of SPE Eastern Regional Conference, SPE Paper, October, 2001. ##Anifowose, F. A. and Abdulraheem, A., Prediction of Porosity and Permeability of Oil and Gas Reservoirs Using Hybrid Computational Intelligence Models, in North Africa Technical Conference and Exhibition, Society of Petroleum Engineers, 2010. ##Anifowose, F. A., Ewenla, A. O., and Eludiora, S. I., Prediction of Oil and Gas Reservoir Properties Using Support Vector Machines, in International Petroleum Technology Conference, International Petroleum Technology Conference, 2011. ##ArevaloVillagran, J. A., Ganpule, S. V., Wattenbarger, R. A., SamaniegoVerduzco, F., YanezMondragon, M., and SerranoLozano, J. R., Analysis of Longterm Performance in Tight Gas Wells: Field Examples, in SPE International Petroleum Conference and Exhibition in Mexico, Society of Petroleum Engineers, 2002. ##Asadisaghandi, J. and Tahmasebi, P. Comparative Evaluation of Backpropagation Neural Network Learning Algorithms and Empirical Correlations for Prediction of Oil PVT Properties in Iran Oilfields, Journal of Petroleum Science and Engineering, Vol. 78, p. 464475, 2011. ##Baziar, S., Tadayoni, M., NabiBidhendi, M., and Khalili, M., Prediction of Permeability in a Tight Gas Reservoir by Using Three Soft Computing Approaches: A Comparative Study, Journal of Natural Gas Science and Engineering, Vol. 21, p. 718724, 2014. ##Becker, C. J., Christoudias, C. M., and Fua, P., Nonlinear Domain Adaptation with Boosting, in Advances in Neural Information Processing Systems, Vol. 17, p. 485493,2013. ##Bhatt, A., Reservoir Properties from Well Logs Using Neural Networks, 2002. ##Carrasquilla, A., Silvab, J., and Flexac, R. Associating Fuzzy Logic, Neural Networks and Multivariable Statistic Methodologies in the Automatic Identification of Oil Reservoir Lithologies through Well Logs, Revista de Geologia, Vol. 21, p. 2734, 2008. ##Chang, H. C., Chen, H. C., and Fang, J. H., Lithology Determination from Well Logs with Fuzzy Associative Memory Neural Network, Geoscience and Remote Sensing, IEEE Transactions on Vol. 35, p. 773780, 1997, ##Cristianini, N. and ShaweTaylor, J., An Introduction to Support Vector Machines and other Kernelbased Learning Methods, Cambridge University Press, 2000. ##Cumella, S. P. and Scheevel, J., The Influence of Stratigraphy and Rock Mechanics on Mesaverde Gas Distribution, Piceance Basin, Colorado, 2008. ##Friedman, J. H., Stochastic Gradient Boosting, Computational Statistics and Data Analysis, Vol. 38, p. 367378, 1999. ##Goda, H. M., Maier, H., and Behrenbruch, P., Use of Artificial Intelligence Techniques for Predicting Irreducible Water SaturationAustralian Hydrocarbon Basins, in Asia Pacific Oil and Gas Conference and Exhibition, Society of Petroleum Engineers, 2007. ##Haykin, S., Neural Networks, a Comprehensive Foundation, Prentice Hall PTR. ##Huang, Z., Shimeld, J., Williamson, M., and Katsube, J., Permeability Prediction with Artificial Neural Network Modeling in the Venture Gas Field, Offshore Eastern Canada., Geophysics, Vol. 61, p. 422436, 1994. ##Huber, P. J. Robust Estimation of a Location Parameter, the Annals of Mathematical Statistics, Vol. 35, p. 73101, 1964. ##Ibrahim, M. A. and Potter, D. K., Prediction of Residual Water Saturation Using Genetically Focused Neural Nets, in SPE Asia Pacific Oil and Gas Conference and Exhibition, Society of Petroleum Engineers, 2004. ##Karimpouli, S., Fathianpour, N., and Roohi, J., A New Approach to Improve Neural Networks' Algorithm in Permeability Prediction of Petroleum Reservoirs using Supervised Committee Machine Neural Network (SCMNN), Journal of Petroleum Science and Engineering, Vol. 73, p. 227232, 2010. ##Kecman, V., Support Vector Machines–an Introduction, in Support Vector Machines, Theory and Applications, Springer, 2005. ##Mollajan, A. and Memarian, H., Estimation of Water Saturation from Petrophysical Logs Using Radial basis Function Neural Network, Journal of Tethys, Vol. 1, p. 156163, 2013. ##Naik, G., Tight Gas Reservoirs–an Unconventional Natural Energy Source for the Future, www. Sublettese. org/files/tight_gas. pdf. Accessado em, Vol. 1, p. 17, 2008. ##Parrella, F., Online Support Vector Regression, Master's Degree Thesis, Department of Information Science, University of Genoa, Italy, 2007. ##Saffarzadeh, S. and Shadizadeh, S. R., Reservoir Rock Permeability Prediction Using Support Vector Regression in an Iranian Oil Field, Journal of Geophysics and Engineering, Vol. 9, No. 336, p. 336344, 2012. ##Shokir, E. E. M., Prediction of the Hydrocarbon Saturation in Low Resistivity Formation via Artificial Neural Network, in SPE Asia Pacific Conference on Integrated Modeling for Asset Management. Society of Petroleum Engineers, 2004. ##Smola, A. J. and Schölkopf, B., A Tutorial on Support Vector Regression, Statistics and Computing, 14, 2004. ##Specht, D. F. Probabilistic Neural Networks, Neural Networks, Vol. 3, p. 109118, 1990. ##Specht, D. F., A General Regression Neural Network, Neural Networks, IEEE Transactions on, Vol. 2, p. 568576, 1991. ##StatSoft, S. N. N., StatSoft Inc. USA, 1998. ##Sun, Q., Eissa, M., Castagna, J., Cersosimo, S., Sun, S. and Decker, C., Porosity from Artificial Neural Network Inversion for Bermejo Field, Ecuador, in SEG Expanded Abstracts, Vol. 734, p. 734741, 2001. ##Tadayoni, M. and Valadkhani, M., New Approach for the Prediction of Klinkenberg Permeability In situ for Low Permeability Sandstone in Tight Gas Reservoir, in SPE Middle East Unconventional Gas Conference and Exhibition, Society of Petroleum Engineers, 2012. ##Vapnik, V. Estimation of Dependences based on Empirical Data, N.Y., SpringerVerlag, 1982. ##Vapnik, V. The Nature of Statistical Learning Theory, Springer, 2000. ##Vapnik, V., Golowich, S. E., and Smola, A., Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing, Advances in Neural Information Processing Systems 1997. ##Vapnik, V. N. and Chervonenkis, A. J., Theory of Pattern Recognition, 1974. ##Wiener, J., Rogers, J., and Moll, B., Predict Permeability from Wireline Logs Using Neural Networks. Petroleum Engineer International, Vol. 68, p. 6875, 1995. ##Wong, P., Jian, F., and Taggart, I., A Critical Comparison of Neural Networks and Discriminant Analysis in Lithofacies, Porosity and Permeability Predictions, Journal of Petroleum Geology, Vol. 18, p. 191206, 1995. ##Wong, P. M., Jang, M., Cho, S., and Gedeon, T. D., Multiple Permeability Predictions Using an Observational Learning Algorithm, Computers and Geosciences, Vol. 26, p. 907913, 2000. ##]
Determination of Pore Pressure from Sonic Log: a Case Study on One of Iran Carbonate Reservoir Rocks
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Pore pressureis defined as the pressure of the fluid inside the pore space of the formation, which is also known as the formation pressure. When the pore pressure is higher than hydrostatic pressure, it is referred to as overpressure. Knowledge of this pressure is essential for costeffective drilling, safe well planning, and efficient reservoir modeling. The main objective of this study is to estimate the formation pore pressure as a reliable mud weight pressure using well log data at one of oil fields in the south of Iran. To obtain this goal, the formation pore pressure is estimated from well logging data by applying Eaton’s prediction method with some modifications. In this way, sonic transient time trend line is separated by lithology changes and recalibrated by Weakley’s approach. The created sonic transient time is used to create an overlay pore pressure based on Eaton’s method and is led to pore pressure determination. The results are compared with the pore pressure estimated from commonly used methods such as Eaton’s and Bowers’s methods. The determined pore pressure from Weakley’s approach shows some improvements in comparison with Eaton’s method. However, the results of Bowers’s method, in comparison with the other two methods, show relatively better agreement with the mud weight pressure values.
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37
50


Morteza
Azadpour
Department of Petroleum Exploration Engineering, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Department of Petroleum Exploration Engineering,
Iran
m.azadpour67@gmail.com


Navid
Shad Manaman
Department of Petroleum Exploration Engineering, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Department of Petroleum Exploration Engineering,
Iran
shmanaman@ut.ac.ir
pore pressure
Welllogging
Weakley’s Approach
Eaton’s Method
Carbonate Reservoirs
Solubility of Methane, Ethane, and Propane in Pure Water Using New Binary Interaction Parameters
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2
Solubility of hydrocarbons in water is important due to ecological concerns and new restrictions on the existence of organic pollutants in water streams. Also, the creation of a thermodynamic model has required an advanced study of the phase equilibrium between water (as a basis for the widest spread muds and amines) and gas hydrocarbon phases in wide temperature and pressure ranges. Therefore, it is of great interest to develop semiempirical correlations, charts, or thermodynamic models for estimating the solubility of hydrocarbons in liquid water. In this work, a thermodynamic model based on Mathias modification of SovaRedlichKwong (SRK) equation of state is suggested using classical mixing rules with new binary interaction parameters which were used for twocomponent systems of hydrocarbons and water. Finally, the model results and their deviations in comparison with the experimental data are presented; these deviations were equal to 5.27, 6.06, and 4.1% for methane, ethane, and propane respectively.
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51
59


Masoud
Behrouz
Department of Gas Engineering, Petroleum University of Technology, Ahwaz, Iran
Department of Gas Engineering, Petroleum
Iran
masoud.behrouz806@gmail.com


Masoud
Aghajani
Department of Gas Engineering, Petroleum University of Technology, Ahwaz, Iran
Department of Gas Engineering, Petroleum
Iran
m.aghajani@put.ac.ir
Methane
Ethane
Propane
Light Hydrocarbons
solubility
A Decision Support System (DSS) to Select the Premier Fuel to Develop in the Value Chain of Natural Gas
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2
A value chain is a series of events that takes a raw material and with each step adds value to it. Global interest in the application of natural gas (NG) in production and transportation has grown dramatically, representing a longterm, lowcost, domestic, and secure alternative to petroleumbased fuels. Many technological solutions are currently considered on the market or in development, which address the challenge and opportunity of NG. In this paper, a decision support system (DSS) is introduced for selecting the best fuel to develop in the value chain of NG through four options, namely compressed NG (CNG), liquefied NG (LNG), dimethyl ether (DME), and gastoliquids (GTL). The DSS includes a model which uses the technique for order performance by similarity to ideal solution (TOPSIS) to select the best fuel in the value chain of NG based on the attributes such as market situations, technology availability, and transportation infrastructure. The model recommends some key guidelines for two branches of countries, i.e. those which have NG resources and the others. We believe that applying the proposed DSS helps the oil and gas/energy ministries in a most effective and productive manner dealing with the complicated fuelrelated production and transportation decisionmaking situations.
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60
76


Ahmad
Mousaei
Department of Market Research, Research Institute of Petroleum Industry, Tehran, Iran
Department of Market Research, Research Institute
Iran
mousaeia@ripi.ir


Mohammad Ali
Hatefi
Department of Energy Economics & Management, Petroleum University of Technology, Tehran, Iran
Department of Energy Economics & Management,
Iran
Natural gas
CNG
LNG
GTL
DME
DSS
TOPSIS
MADM
A CFD Simulation of the Parameters Affecting the Performance of Downhole Deoiling Hydrocyclone
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2
Among the all parameters affecting the performance of a downhole deoiling hydrocyclone, the investigation of internal flow field deserves more attempts especially in the petroleum industry. In this study, the effects of inlet flow rate, inlet oil volume fraction, and oil droplet diameter on the separation efficiency and pressure drop ratio have been investigated along the hydrocyclone body. All the simulations were performed using computational fluid dynamics (CFD) techniques, in which the Eulerian multiphase model and the Reynolds stress turbulent model were employed for the prediction of multiphase and turbulent flow parameters through the hydrocyclone. The velocity component profiles, separation efficiency, pressure drop, and volume fraction are also other parameters which have been considered in this work. The results of the simulations illustrate good agreement with the reported experimental data. Furthermore, the simulations indicate that the separation efficiency almost increases twofold, when the droplet diameter increases from 25 to 50 micron. The effect of inlet flow rate on the separation efficiency is so significant that an increase in inlet flow rate from 5 to 25 l/min causes a sharp increase in the separation efficiency and raises it 2.5 times the initial value. However, the inlet oil volume fraction showed a minor effect on the hydrodynamic flow behavior in the hydrocyclone body compared to the other investigated parameters.
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77
93


Seyyed Mohsen
Hosseini
Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran
Department of Petroleum Engineering, Petroleum
Iran
hosseini.m@put.ac.ir


Khalil
Shahbazi
Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran
Department of Petroleum Engineering, Petroleum
Iran
shahbazi@put.ac.ir


Mohammad Reza
Khosravi Nikou
Department of Gas Engineering, Petroleum University of Technology, Ahwaz, Iran
Department of Gas Engineering, Petroleum
Iran
Separation Efficiency
Computational Fluid Dynamics
Pressure drop