ORIGINAL_ARTICLE
The Heinemann-Mittermeir Generalized Shape Factor and Its Practical Relevance
Fifty years ago Warren and Root have introduced the shape factor. This fundamental parameter for modeling of naturally fractured reservoirs has been discussed stormily ever since. Different definitions for shape factor have been suggested which all of them are heuristically based. Recently, Heinemann and Mittermeir mathematically derived - based on the dual-continuum theorem assuming pseudo-steady state condition- a general and proper form of the shape factor formula which can be simplified to the previously published shape factor definitions. This paper discusses the practical relevance of the Heinemann-Mittermeir formula. Its difference to the most commonly used Kazemi et al.formula is its demonstration by fine-scale single matrix block simulation. Furthermore, it is shown that the generally applied isotropy assumption can lead to significantly wrong results. Consequently, the generalized Heinemann-Mittermeir shape factor formula is recommended to be routinely practiced in the industry for more accurate results. The paper tries to present a proper realization of the nature of the shape factor as well as presentation of detailed mathematical and practical approaches for measuring all the required values in order to determine the shape factor for individual matrix rock pieces from outcrops of fractured formations. Performing those measurements routinely is regarded as essential parameter for its usability.
https://jchpe.ut.ac.ir/article_5582_e48ce8d2333edc51b6a8243cb36b3ac9.pdf
2014-06-01
1
13
10.22059/jchpe.2014.5582
Shape factor
Fractured reservoirs
Transfer function
Heinemann- Mittermeir
Single matrix block
Mohammad Taghi
Amiry
mohammad@amiry.net
1
Leoben Mining University, Austria
LEAD_AUTHOR
Zoltan
E. Heinemann
2
Leoben Mining University, Austria
AUTHOR
Clemens
Brand
3
Leoben Mining University, Austria
AUTHOR
[1] Barenblatt, G. J., Zheltov, I. P. and Kochina, I. N. (1960). "Basic concepts in the theory of seepage of homogeneous liquids in fissured rocks." J. Appl. Math. Mech., Vol. 24, pp. 1286-1303.
1
[2] Warren, J.E. and Root, P.J. (1963). The Behavior of Naturally Fractured Reservoirs, SPE Journal, (Sept.1963), 245-255.
2
[3] Kazemi, H., Merrill, L.S., Porterfield, K.L. and Zeman, P.R. (1976). Numerical Simulation of Water-Oil Flow in Naturally Fractured Reservoirs, SPE Journal, 317-326.
3
[4] Coats, K.H. (1989). Implicit Compositional Simulation of Single-Porosity and Dual-Porosity Reservoirs. SPE paper 18427.
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[5] Quintard, M. and Whitaker, S. (1996). "Theoretical development of region-averaged equations for slightly compressible single-phase flow." Adv. Water Res., Vol. 19(1), pp. 29-47.
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[6] Zimmerman, R.W., Chen, G., Hadgu, T. and Bodvarsson, G.S. (1993). "A numerical dual-porosity model with semi-analytical treatment of fracture/matrix flow." Wat. Resour. Res., Vol. 29(7), pp. 2127-2137.
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[7] Mathias, S.A. and Zimmerman, R.W. (2003). Laplace transform inversion for late-time behavior of groundwater flow problems. Water Resources Research, 39(10): paper 1283.
7
[8] Kazemi, H., Gilman, J.R. and Elsharkawy, A.M. (1992). Analytical and Numerical Solution of Oil Recovery from Fractured Reservoirs Using Empirical Transfer Functions, SPE Reservoir Engineering, May 1992, 219-227
8
[9] Heinemann, Z.E. and Mittermeir, G.M. (2012). "Derivation of the Kazemi-Gilman-Elsharkawy generalized dual porosity shape factor." Transp. Porous Med., Vol. 91(1), pp. 123-132.
9
[10] de Swaan, A. (1976). Analytic Solutions for Determining Naturally Fracture Reservoir Properties by Well Testing. SPE Paper, 5346-PA.
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[11] de Swaan, A. (1990). Influence of Shape and Skin of Matrix-Rock Blocks on Pressure Transients in Fractured Reservoirs. SPE Formation Evaluation, Dec. 1990, 344-352.
11
[12] Sarma, P. and Aziz, K. (2006). Production Optimization With Adjoint Models Under Nonlinear ControlState Path Inequality Constraints. SPE Paper, 99959-MS.
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[13] Lim, KT. And Aziz, K. (1995). "Matrix-fracture transfer shape factors for dual-porosity simulators." J. Pet. Sci. Eng, Vol. 13, pp. 169-178.
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[14] Schlumberger, (2012). ECLIPSE reservoir simulation software – Technical Description Version 2012.1.
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[15] Barker, J.A. (1985). "Block-geometry functions characterizing transport in densely fissured media." J. Hydrol., Vol. 77, pp. 263-279.
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[16] Dunham, W. (1990). Heron's Formula for Triangular Area. Ch. 5 in Journey through Genius: The Great Theorems of Mathematics. New York: Wiley, pp. 113-132.
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[17] Wolfram Math World, http://mathworld.wolfram.com/Point-PlaneDistance.html
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[18] Lishman, J.R. (1970). Core Permeability Anisotropy, J. Canadian Pet. Tech., 9(2).
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[19] Mousatov, A., Pervago, E. and Shevnin, V. (2000). A New Approach to Resistivity Anisotropy Measurements, SEG 2000 Expanded Abstracts, 2000-1381.
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[20] Durlofsky, L.J. (1991). "Numerical calculation of equivalent grid block permeability tensors for heterogeneous porous media." Wat. Resou. Res., Vol. 27(5), pp. 699-708.
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[21] Rose, W. (1982). "A new method to measure directional permeability." J. Petrol. Technol., May 1982. 22- Walter D., R. (1982). "A new method to measure directional permeability." J. Petrol. Eng., Vol. 34(5), pp. 1142-1144.
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[23] Weitzenböck, J.R., Shenoi, R.A. and Wilson, P.A. (1997). "Measurement of three-dimensional permeability." Appl. Sci. Manuf., Vol. 29, pp. 159-169.
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[24] Asadi, M., Ghalambor, A., Rose, W.D. and Shirazi, M. K. (2000). Anisotropic Permeability Measurement of Porous Media: A 3-Dimensional Method. SPE Conference Paper, 59396.
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[25] Onsager, L. (1931). "Reciprocal relations in irreversible processes." Phys. Rev. Vol. 37, pp. 405-426.
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[26] Anton, H. and Rorres, C. (2000). Elementary Linear Algebra (Applications Version) 8th edition, John Wiley & Sons. ISBN 978-0-471-17052-5.
25
[27] Muskat, M. (1937). The flow of homogeneous fluids through porous media, Mc Graw Hill Book Company Inc., NY, USA, 137-148.
26
[28] Creative Dimension Software Ltd – 3D Software Modeller Pro.
27
ORIGINAL_ARTICLE
A Numerical Study on Agglomeration in High Temperature Fluidized beds
Soft-sphere discrete element method (DEM) and Navier-Stokes equations were coupled with equations of energy for gas and solids to investigate the process of agglomeration in fluidized bed of polyethylene particles at high temperature. The Newton’s second law of motion was adapted for translational and rotational motion of particles and agglomerates. The cohesive force for polyethylene particles was calculated based on a time dependent model for solid bridging by the viscous flow mechanism. The motion of agglomerates was described by means of the multi-sphere method. By taking into account the cohesiveness of particles at high temperatures and considering real dynamic agglomerates, the fluidization behavior of a bed of polyethylene particles was successfully simulated in terms of increasing the size of agglomerates. Effect of the inlet gas temperature on mass and size of agglomerates was investigated. A mechanistic study in terms of contact time, cohesive force and repulsive force, which are the key parameters in the formation of agglomerates, were also carried out.
https://jchpe.ut.ac.ir/article_5583_0d09160cc1aaf367c68b38a84d406553.pdf
2014-06-01
15
25
10.22059/jchpe.2014.5583
Agglomeration
Discrete element method
Fluidized bed
High temperature solid bridge
Zahra
Mansourpour
mansourp@ut.ac.ir
1
School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Navid
Mostoufi
mostoufi@ut.ac.ir
2
University of Tehran
AUTHOR
Rahmat
Sotudeh-Gharebagh
3
School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Anderson, T.B. and Jackson, R.A. (1967). "Fluid mechanical description of fluidized beds: Equations of motion." Ind. Eng. Chem. Fund., Vol. 6, pp. 527-539.
1
[2] Cundall, P.A. and Strack, O.D.L. (1979). "A discrete numerical model for granular assemblies." Geo technique, Vol. 29, pp. 47–65.
2
[3] Deen, N.G., Annaland, Van Sint M., Van der Hoef, M.A. and Kuipers, J.A.M. (2007). "Review of discrete particle modeling of fluidized beds." Chem. Eng. Sci., Vol. 62, pp. 28-44.
3
[4] Mikami, T., Kamiya, H. and Horio, M. (1998). "Numerical simulation of cohesive powder behavior in a fluidized bed." Chem. Eng. Sci., Vol. 53 No. 10, pp.1927-1940.
4
[5] Iwadate, Y. and Horio, M. (1998). "Prediction of agglomerate sizes in bubbling fluidized beds of group C powder." Powder Technol., Vol. 100, pp. 223-236.
5
[6] Li, Sh., Marshall, J. S., Liu, G. and Yao, Q. (2013) "Adhesive particulate flow: The discrete-element method and its application in energy and environmental engineering.” Progr. Energ. Combust. Sci., Vol. 37, pp. 633-668.
6
[7] Kuwagi, K., Mikami, T. and Horio, M. (2000). "Numerical simulation of metallic solid bridge particles in a fluidized bed at high temperature." Powder Technol., Vol. 109, pp.27-40.
7
[8] Mansourpour, Z., Mostoufi, N. and Sotoudeh-Gharebagh, R. (2013). "A mechanistic study of agglomeration in fluidized beds at elevated pressures." The Canadian J. Chem. Eng., Vol. 91, pp. 560-569.
8
[9] Mansourpour, Z., Mostoufi, N. and Sotoudeh-Gharebagh, R. (2014). "Investigating agglomeration phenomena in an air-polyethylene fluidized bed using DEM–CFD approach." Chem. Eng. Res. Des., Vol. 92, pp. 102-118.
9
[10] Mansourpour, Z., Karimi, S. Zarghami, R., Mostoufi, N. and Sotoudeh-Gharebagh, R. (2010). "Insights in hydrodynamics of bubbling fluidized beds at elevated pressure by DEM–CFD approach." Particuology, Vol. 8, pp. 404-414.
10
[11] Kruggel-Emden, H., Rickelt, S. Wirtz, S. and Scherer, V. (2008). "A study on the validity of the multisphere discrete element method." Powder Technol., Vol. 188, pp. 153-165.
11
[12] Seville, J.P.K., Willett, C.D. and Knight, P.C. (2000). "Interparticle forces in fluidization: a review." Powder Technol., Vol. 113, pp. 261-268.
12
[13] Seville, J.P.K., Silomon-Pflug, H. and Knight, P.C. (1998). "Modeling of sintering in high temperature gas fluidization." Powder Technol., Vol. 97, pp.160-169.
13
ORIGINAL_ARTICLE
Estimation of Binary Infinite Dilute Diffusion Coefficient Using Artificial Neural Network
In this study, the use of the three-layer feed forward neural network has been investigated for estimating of infinite dilute diffusion coefficient ( D12 ) of supercritical fluid (SCF), liquid and gas binary systems. Infinite dilute diffusion coefficient was spotted as a function of critical temperature, critical pressure, critical volume, normal boiling point, molecular volume in normal boiling point, molecule diameter, Lennard-Jones’s (LJ) energy parameter, temperature and pressure. For each set of SCF, liquid and gas systems a three-layer network has been applied with training algorithm of Levenberg-Marquard (LM). The obtained results of models have shown good accuracy of artificial neural network (ANN) for estimating infinite dilute diffusion coefficient of SCF, liquid and gas binary systems with mean relative error (MRE) of 2.88 % for 231 systems containing 4078 data points (mean relative error for ANN model in SCF, liquid and gas binary systems are 3.00, 2.99 and 1.21 %, respectively)
https://jchpe.ut.ac.ir/article_5584_fd15a49035b5a2f9d471071d75a05576.pdf
2014-06-01
27
45
10.22059/jchpe.2014.5584
Artificial Neural Network
Binary mixture
Infinite dilute diffusion coefficient
Supercritical Fluid
Majid
Mohadesi
m.mohadesi@kut.ac.ir
1
Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
LEAD_AUTHOR
Gholamreza
Moradi
2
Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
AUTHOR
Hosnie-Sadat
Mousavi
mousavi_h_1986@yahoo.com
3
Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
AUTHOR
[1] Reid, R.C., Prausnitz, J. M. and Poling, B.E. (2001). The Properties of Gases and Liquids. 5th. Ed. McGrawHill Pub. Co., New York.
1
[2] Magalhaes, A.L., Da Silva, F.A. and Silva, C.M. (2011). “New models for tracer diffusion coefficients of hard sphere and real systems: Application to gases, liquids and supercritical fluids.” J. Supercrit. Fluids, Vol. 55, pp. 898–923.
2
[3] Bhat, N. and McAvoy, T.J. (2000). “Use of neural nets for dynamic modeling and control of chemical process systems.” Comput. Chem. Eng., Vol. 14, pp. 573-583.
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[4] Pollard, J.F., Broussard, M.R., Garrison, D.B. and San, K.Y. (1992). “Process identification using neural networks.” Comput. Chem. Eng., Vol. 16, pp. 253-270.
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[5] Psichogios, D.C. and Ungar, L.H. (1992). “A hybrid neural network-first principles approach to process modeling.” AIChE J., Vol. 38, pp. 1499-1511.
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[6] Wang, H., Oh, Y. and Yoon, E.S. (1998). “Strategies for modeling and control of nonlinear chemical processes using neural networks.” Comput. Chem. Eng., Vol. 22, pp. 823-826.
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[8] Fissore, D., Barresi, A.A. and Manca, D. (2004). “Modeling of methanol synthesis in a network of forced unsteady-state ring reactors by artificial neural networks for control purposes.” Chem. Eng. Sci., Vol. 59, pp. 4033-4041.
8
[9] Kito, S., Satsuma, A., Ishikura, T., Niwa, M., Murakami, Y. and Hattori, T. (2004). “Application of neural network to estimation of catalyst deactivation in methanol conversion.” Catal. Today, Vol. 97, pp. 41-47.
9
[10] Papadokonstantakis, S., Machefer, S., Schnitzleni, K. and Lygeros, A.I. (2005). “Variable selection and data pre-processing in NN modeling of complex chemical processes.” Comput. Chem. Eng., Vol. 29, pp. 1647-1659.
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[11] Omata, K., Nukai, N. and Yamada, M. (2005). “Artificial neural network aided design of a stable Co-MgO catalyst of high-pressure dry reforming of methane.” Ind. Eng. Chem. Res., Vol. 44, pp. 296-301.
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[12] Himmelblau, D., (2008). “Accounts of experiences in the application of artificial neural networks in chemical engineering.” Ind. Eng. Chem. Res., Vol. 47, pp. 5782-5796.
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[13] Eslamloueyan, R. and Khademi, M.H. (2009). “Estimation of thermal conductivity of pure gases by using artificial neural networks.” Int. J. Thermal Sci., Vol. 48, pp. 1094–1101.
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[14] Eslamloueyan, R. and Khademi, M.H. (2009). “Using artificial neural networks for estimation of thermal conductivity of binary gaseous mixtures.” J. Chem. Eng. Data, Vol. 54, pp. 922–932.
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[15] Eslamloueyan, R. and Khademi, M.H. (2010). “A neural network-based method for estimation of binary gas diffusivity.” Chemom. Intell. Lab. Syst., Vol. 104, pp. 195–204.
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[16] Sun, C.K.J. and Chen, S.H. (1985). “Diffusion of benzene, toluene, naphthalene, and Phenanthrene in supercritical dense 2,3-dimethylbutane.” AIChE J., Vol. 31, pp. 1904–1910.
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[17] Suarez-Iglesias, O., Medina, I., Pizarro, C. and Bueno, J.L. (2007). “Diffusion coefficients of 2-fluoroanisole, 2-bromoanisole, allylbenzene and 1,3-divinylbenzene at infinite dilution in supercritical carbon dioxide.” Fluid Phase Equilib., Vol. 260, pp. 279–286.
17
[18] Pizarro, C., Suarez-Iglesias, O., Medina, I. and Bueno, J.L. (2009). “Binary diffusion coefficients for 2,3- dimethylaniline, 2,6-dimethylaniline, 2-methylanisole, 4-methylanisole and 3-nitrotoluene in supercritical carbon dioxide.” J. Supercrit. Fluids, Vol. 48, pp. 1–8.
18
[19] Kong, C.Y., Withanage, N.R.W., Funazukuri, T. and Kagei, S. (2006). “Binary diffusion coefficients and retention factors for γ-linolenic acid and its methyl and ethyl esters in supercritical carbon dioxide.” J. Supercrit. Fluids, Vol. 37, pp. 63–71.
19
[20] Funazukuri, T., Hachisu, S. and Wakao, N. (1991). “Measurements of binary diffusion coefficients of C16– C24 unsaturated fatty acid methyl esters in supercritical carbon dioxide.” Ind. Eng. Chem. Res., Vol. 30, pp. 1323–1329.
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[21] Yang, Y.N. and Matthews, M.A. (2001). “Diffusion of chelating agents in supercritical CO2 and a predictive approach for diffusion coefficients.” J. Chem. Eng. Data, Vol. 46, pp. 588–595.
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[22] Gonzalez, K.M., Bueno, J.L. and Medina, I. (2002). “Measurement of diffusion coefficients for 2- nitroanisole, 1,2-dichlorobenzene and tert-butylbenzene in carbon dioxide containing modifiers.” J. Supercrit. Fluids, Vol. 24, pp. 219–229.
22
[23] Pizarro, C., Suarez-Iglesias, O., Medina, I. and Bueno, J.L. (2007). “Using supercritical fluid chromatography to determine diffusion coefficients of 1,2-diethylbenzene, 1,4-diethylbenzene, 5-tert-butylm-xylene and phenylacetylene in supercritical carbon dioxide.” J. Chromatogr., A, Vol. 1167, pp. 202–209.
23
[24] Sassiat, P.R., Mourier, P., Caude, M.H. and Rosset, R.H. (1987). “Measurement of diffusion coefficients in supercritical carbon dioxide and correlation with the equation of Wilke and Chang.” Anal. Chem., Vol. 59, pp. 1164–1170.
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[25] Gonzalez, L.M., Suarez-Iglesias, O., Bueno, J.L., Pizarro, C. and Medina, I. (2007). “Limiting binary diffusivities of aniline, styrene, and mesitylene in supercritical carbon dioxide.” J. Chem. Eng. Data, Vol. 52, pp. 1286–1290.
25
[26] Kong, C.Y., Takahashi, N., Funazukuri, T. and Kagei, S. (2007). “Measurements of binary diffusion coefficients and retention factors for dibenzo-24-crown-8 and 15-crown-5 in supercritical carbon dioxide by chromatographic impulse response technique.” Fluid Phase Equilib., Vol. 257, pp. 223–227.
26
[27] Pizarro, C., Suarez-Iglesias, O., Medina, I. and Bueno, J.L. (2008). “Diffusion coefficients of nbutylbenzene, n-pentylbenzene, 1-phenylhexane, 1-phenyloctane, and 1-phenyldodecane in supercritical carbon dioxide.” Ind. Eng. Chem. Res., Vol. 47, pp. 6783–6789.
27
[28] Pizarro, C., Suarez-Iglesias, O., Medina, I. and Bueno, J.L. (2008). “Molecular diffusion coefficients of phenylmethanol, 1-phenylethanol, 2-phenylethanol, 2-phenyl-1-propanol, and 3-phenyl-1-propanol in supercritical carbon dioxide.” J. Supercrit. Fluids, Vol. 43, pp. 469–476.
28
[29] Kong, C.Y., Funazukuri, T. and Kagei, S. (2006). “Binary diffusion coefficients and retention factors for polar compounds in supercritical carbon dioxide by chromatographic impulse response method.” J. Supercrit. Fluids, Vol. 37, pp. 359–366.
29
[30] Gonzalez, L.M., Bueno, J.L. and Medina, I. (2001). “Determination of binary diffusion coefficients of anisole, 2,4-dimethylphenol, and nitrobenzene in supercritical carbon dioxide.” Ind. Eng. Chem. Res., Vol. 40, pp. 3711–3716.
30
[31] Funazukuri, T., Kong, C.Y. and Kagei, S. (2000). “Infinite-dilution binary diffusion coefficients of 2- propanone, 2-butanone, 2-pentanone, and 3-pentanone in CO2 by the Taylor dispersion technique from 308.15 to 328.15K in the pressure range from 8 to 35 MPa.” Int. J. Thermophys., Vol. 21, pp. 1279–1290.
31
[32] Pizarro, C., Suarez-Iglesias, O., Medina, I. and Bueno, J.L. (2009). “Binary diffusion coefficients of 2-ethyltoluene, 3-ethyltoluene, and 4-ethyltoluene in supercritical carbon dioxide.” J. Chem. Eng. Data, Vol. 54, pp. 1467–1471.
32
[33] Suarez-Iglesias, O., Medina, I., Pizarro, C. and Bueno, J.L. (2007). “Diffusion of benzyl acetate, 2- phenylethyl acetate, 3-phenylpropyl acetate, and dibenzyl ether in mixtures of carbon dioxide and ethanol.” Ind. Eng. Chem. Res., Vol. 46, pp. 3810–3819.
33
[34] Funazukuri, T., Kong, C.Y. and Kagei, S. (2003). “Binary diffusion coefficient, partition ratio, and partial molar volume for docosahexaenoic acid, eicosapentaenoic acid and γ-linolenic acid at infinite dilution in supercritical carbon dioxide.” Fluid Phase Equilib., Vol. 206, pp. 163–178.
34
[35] Han, Y.S., Yang, Y.W. and Wu, P.D. (2007). “Binary diffusion coefficients of Arachidonic acid ethyl ester, cis-5,8,11,14,17-eicosapentaenoic acid ethyl ester, and cis-4,7,10,13,16,19-docosahexanenoic acid ethyl esthers in supercritical carbon dioxide.” J. Chem. Eng. Data, Vol. 52, pp. 555–559.
35
[36] Funazukuri, T., Kong, C.Y. and Kagei, S. (2000). “Binary diffusion coefficients of acetone in carbon dioxide at 308.2 and 313.2K in the pressure range from 7.9 to 40 MPa.” Int. J. Thermophys., Vol. 21, pp. 651–669.
36
[37] Kong, C.Y., Funazukuri, T. and Kagei, S. (2004). “Chromatographic impulse response technique with curve fitting to measure binary diffusion coefficients and retention factors using polymer-coated capillary columns.” J. Chromatogr., A, Vol. 1035, pp. 177–193.
37
[38] Shenai, V.M., Hamilton, B.L. and Matthews, M.A. (1993). “Diffusion in liquid and supercritical fluid mixtures.” ACS Symp. Ser., Vol. 514, pp. 92–103.
38
[39] Silva, C.M., Filho, C.A., Quadri, M.B. and Macedo, E.A. (2004). “Binary diffusion coefficients of α-pinene and β-pinene in supercritical carbon dioxide.” J. Supercrit. Fluids, Vol. 32, pp. 167–175.
39
[40] Funazukuri, T., Kong, C.Y., Kikuchi, T. and Kagei, S. (2003). “Measurements of binary diffusion coefficient and partition ratio at infinite dilution for linoleic acid and arachidonic acid in supercritical carbon dioxide.” J. Chem. Eng. Data, Vol. 48, pp. 684–688.
40
[41] Funazukuri, T., Kong, C.Y. and Kagei, S. (2003). “Binary diffusion coefficients, partition ratios and partial molar volumes at infinite dilution for β-carotene and α-tocopherol in supercritical carbon dioxide.” J. Supercrit. Fluids, Vol. 27, pp. 85–96.
41
[42] Funazukuri, T., Kong, C.Y., Murooka, N. and Kagei, S. (2000). “Measurements of binary diffusion coefficients and partition ratios for acetone, phenol, α-tocopherol, and β-carotene in supercritical carbon dioxide with a poly (ethylene glycol)-coated capillary column.” Ind. Eng. Chem. Res., Vol. 39, pp. 4462–4469.
42
[43] Funazukuri, T., Kong, C.Y. and Kagei, S. (2002). “Measurements of binary diffusion coefficients for some low volatile compounds in supercritical carbon dioxide by input-output response technique with two diffusion columns connected in series.” Fluid Phase Equilib., Vol. 194, pp. 1169–1178.
43
[44] Liong, K.K., Wells, P.A. and Foster, N.R. (1991). “Diffusion coefficients of long-chain esters in supercritical carbon dioxide.” Ind. Eng. Chem. Res., Vol. 30, pp. 1329–1335.
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[45] Liong, K.K., Wells, P.A. and Foster, N.R. (1992). “Diffusion of fatty acid esters in supercritical carbon dioxide.” Ind. Eng. Chem. Res., Vol. 31, pp. 390–399.
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[46] Filho, C.A., Silva, C.M., Quadri, M.B. and Macedo, E.A. (2002). “Infinite dilution diffusion coefficients of linalool and benzene in supercritical carbon dioxide.” J. Chem. Eng. Data, Vol. 47, pp. 1351–1354.
46
[47] Bueno, J.L., Suárez, J.J., Dizy, J. and Medina, I. (1993). “Infinite dilution diffusion coefficients: benzene derivatives as solutes in supercritical carbon dioxide.” J. Chem. Eng. Data, Vol. 38, pp. 344–349.
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[48] Funazukuri, T. and Nishimoto, N. (1996). “Tracer diffusion coefficients of benzene in dense CO2 at 313.2K and 8.5–30 MPa.” Fluid Phase Equilib., Vol. 125, pp. 235–243.
48
[49] Funazukuri, T., Kong, C.Y. and Kagei, S. (2001). “Infinite dilution binary diffusion coefficients of benzene in carbon dioxide by the Taylor dispersion technique at temperatures from 308.15 to 328.15K and pressures from 6 to 30MPa.” Int. J. Thermophys., Vol. 22, pp. 1643–1660.
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[50] Sengers, J.M.H.L., Deiters, U.K., Klask, U., Swidersky, P. and Schneider, G.M. (1993). “Application of the Taylor dispersion method in supercritical fluids.” Int. J. Thermophys., Vol. 14, pp. 893–922.
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[51] Fu, H., Coelho, L.A.F. and Matthews, M.A. (2000). “Diffusion coefficients of model contaminants in dense CO2.” J. Supercrit. Fluids, Vol. 18, pp. 141–155 (2000).
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[52] Suarez-Iglesias, O., Medina, I., Pizarro, C. and Bueno, J.L. (2008). “Limiting diffusion coefficients of ethyl benzoate, benzylacetone, and eugenol in carbon dioxide at supercritical conditions.” J. Chem. Eng. Data, Vol. 53, pp. 779–784.
52
[53] Gonzalez, L.M., Suarez-Iglesias, O., Bueno, J.L., Pizarro, C. and Medina, I. (2007). “Application of the corresponding states principle to the diffusion in CO2.” AIChE J., Vol. 53, pp. 3054–3061.
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[54] Lai, C.C. and Tan, C.S. (1995). “Measurement of molecular diffusion coefficients in supercritical carbon dioxide using a coated capillary column.” Ind. Eng. Chem. Res., Vol. 34, pp. 674–680.
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[55] Filho, C.A., Silva, C.M., Quadri, M.B. and Macedo, E.A. (2003). “Tracer diffusion coefficients of citral and D-limonene in supercritical carbon dioxide.” Fluid Phase Equilib., Vol. 204, pp. 65–73.
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[56] Kong, C.Y., Gu, Y.Y., Nakamura, M., Funazukuri, T. and Kagei, S. (2010). “Diffusion coefficients of metal acetylacetonates in supercritical carbon dioxide.” Fluid Phase Equilib., Vol. 297, pp. 162–167.
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99
ORIGINAL_ARTICLE
A Novel Screening Technique for Implementation of Intelligent Reservoir Technology
Throughout life cycle of oil production wells, it is imperative to have production optimization and real response time to rapid changes of well conditions and more understanding of subsurface otherwise it is the matter of expenditure losing. Smart well capabilities meet aforementioned issues. However there is a key concern in managers' mind that they have limited budget and several fields' documents in front. They cannot afford smart well technology for all fields because they know that justification through modeling, simulation and economic evaluation is vital but costly and time consuming. They can apply this box only for one filed. How can they select one field among these fields? In this paper we present a novel screening technique by Analytical Hierarchy Process engine. This technique needs criteria and sub-criteria affecting on smart well potential of fields. Application of this screening technique directed us to prioritize four fields to implement smart well completion. Interestingly; the output of this paper can be used for any set of fields throughout the world.
https://jchpe.ut.ac.ir/article_5585_49ad8b0d7bc9ebb451702844dcda5cfa.pdf
2014-06-01
47
55
10.22059/jchpe.2014.5585
Screening criteria
Smart well
Prioritization
Analytical Hierarchy process
Sensitivity analysis
Mahdi
NadriPari
1
Research Institute of Petroleum Industry (RIPI), Tehran, Iran
AUTHOR
Seyyed Mahdia
Motahhari
motaharism@ripi.ir
2
Research Institute of Petroleum Industry (RIPI), Tehran, Iran
AUTHOR
Turaj
Behrouz
behrouzt@ripi.ir
3
Research Institute of Petroleum Industry (RIPI), Tehran, Iran
LEAD_AUTHOR
Seyyed Saleh
Hendi
hendiss@ripi.ir
4
Research Institute of Petroleum Industry (RIPI), Tehran, Iran
AUTHOR
[1] Arashi, A., Konopczynski, M., Nielson, VJ. and Giuliani, C. (2007). “Defining and implementing functional requirements, of an intelligent-well completion system.” SPE 107829 presented at SPE Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires
1
[2] Ebadi, F., Davis, D., Reynolds, M. and Corbett, PWM. (2005).”Screening of reservoir type for optimization of intelligent well design.” SPE 94053 presented at SPE Europec/EAGE Annual Conference, Madrid, Spain
2
[3] Chung Lau, H. (2008). Shell International E&P Inc., "Good practices in progressing a smart well portfolio." IPTC12255 prepared for presentation at the International Petroleum Technology Conference, Kuala Lumpur, Malaysia
3
[4] Behrouz, T., Motahari, M., Nadri, M. and Hendi, S. (2012). "Determining geological, environmental and economical impact weight for oil field prioritization to implement smart well technology (in Farsi)." published in Iranian Journal of Petroleum Geology (Elmi Pajoheshi), Vol.3, No.3.
4
[5] Azar, A. and Rajabzadeh, A. (2009). "Applied Decision Making (MADM Approach)", Negah Publication
5
[6] Pourafshary, P., et al. (2009). "Priority assessment of investment in development of nanotechnology in upstream petroleum industry.", SPE 126101-MS, Saudi Arabia Section Technical Symposium and Exhibition held in AlKhobar, Saudi Arabia.
6
ORIGINAL_ARTICLE
A Theoretical Mass Transfer Approach for Prediction of Droplets Growth Inside Supersonic Laval Nozzle
Proper estimation of droplet growth rate plays a crucial role on appropriate prediction of supersonic separators performance for separation of fine droplets from a gas stream. Up to now, all available researches employ empirical or semi-empirical correlations to define the relationship between droplet growth rate (dr/dt) and other operating variables such as temperatures (T and TL), Pressure (P) and condensation rate (mL). These empirical or semi-empirical equations are developed for pure component systems and should not be extended to binary or multi-components systems. A novel theoretical approach is presented in this article which provides a fundamental equation to find the droplet growth rate by resorting to mass transfer equations. The new model uses a combination of mass transfer equations and mass and energy balances to estimate the droplet growth rate, droplet temperature and condensation rate simultaneously. Although the simulation results indicate that the proposed method provides impressive results when validated with several real experimental data, however, the main advantage of the present approach is that it can be easily extended to binary or multi-components systems. To the best of our knowledge, the proposed approach has not been addressed previously.
https://jchpe.ut.ac.ir/article_5586_58807cc21e712c8e6a80f0fe23574d37.pdf
2014-06-01
57
68
10.22059/jchpe.2014.5586
Condensation
growth rate
Laval nozzle
Mass Transfer
Supersonic separator
Seyed Heydar
Rajaee Shooshtari
1
Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Akbar
Shahsavand
shahsavand@um.ac.ir
2
Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
[1] Yang, Y. and Shen, Sh. (2009). "Numerical simulation on non-equilibrium spontaneous condensation in supersonic steam flow." Int. Commun. Heat. Mass. Tran., Vol.36, No. 3, pp. 902–907.
1
[2] Mahpeykar, M.R. and Teymourtash, A.R. (2004). “Effects of friction factor and inlet stagnation conditions on the self-condensation of steam in a supersonic nozzle.” Sci. Iranica, Vol. 11, No. 4, pp. 269-282.
2
[3] Koo, A. Brooks, G.A. and Nagle, M. (2008). “Nucleation and growth of Mg condensate during supersonic gas quenching.” J. Cryst. Growth, Vol. 310, No.10, pp. 2659–2667.
3
[4] Guha, A. and Young, J.B. (1991). “Time-marching prediction of unsteady condensation phenomena due to supercritical heat addition.” Proc. conf. on Turbomachinery: Latest Developments in a Changing Scene, London, pp. 167-177.
4
[5] Cinar, G., Yilbas, B. and Sunar, S. M. (1997). “Study into nucleation of steam during expansion through a nozzle.” Int. J.Multiphas. Flow., Vol. 23, No. 6, pp. 1171-118.
5
[6] White, A.J. and Hounslow, M.J. (2000). ”Modelling droplet size distributions in poly-dispersed wet-steam flows.” Int. J. Heat. Mass. Tran., Vol.43, No. 11, pp. 1873-1884.
6
[7] Dykas, S. (2001). “Numerical calculation of the steam condensing flow.” Task quarterly, Vol.5, No.4, pp. 519–535.
7
[8] Gerber, A.G. and Kermani, M.J. (2004). “A pressure based Eulerian-Eulerian multi-phase model for nonequilibrium condensation in transonic steam flow.” Int. J. Heat. Mass. Tran., Vol.47, No.10, pp. 2217–2231.
8
[9] Dykas, S. and Wroblewski, W. (2012). “Numerical modelling of steam condensing flow in low and high pressure nozzles.” Int. J. Heat. Mass. Tran., Vol. 55, No. 21, pp. 6191–6199.
9
[10] Moore, M.J., Walters, P.T., Crane, R.I. and Davidson, B.J. (1973). “Predicting the fog drop size in wet steam turbines”, 4th Wet Steam Conf. Institute of Mechanical Engineers (UK), University of Warwick, pp. C37/73.
10
[11] Kermani, M.J. and Gerber, A.G. (2003). “A general formula for the evaluation of thermodynamic and aerodynamic losses in nucleating steam flow.” Int. J. Heat. Mass. Tran., Vol.46, No. 17, pp. 3265–3278
11
[12] Krol, T. (1971). “Results of optical measurements of diameters of drops formed due to condensation of steam in a de Laval nozzle(in polish).” Prace lnstytutu Maszyn Przeplywowych (Trans. Inst. Fluid Flow Machinery), Vol.187, pp. 199
12
[13] Bakhtar, F. and Mohammadi Tochai, M. T. (1980). “An Investigation of Two-Dimensional Flows of Nucleating and Wet Steam by the Time-Marching Method.” Int. J. Heat. Fluid Flow, Vol.2, No. 1, pp. 5–18
13
[14] Treybal, R.E. (1980). “Mass-Transfer Operations” 5th.Ed. McGraw-Hill Pub, New York.
14
ORIGINAL_ARTICLE
Modeling and Experimental Prediction of Wastewater Treatment Efficiency in Oil Refineries Using Activated Sludge Process
In this study, activated sludge process for wastewater treatment in a refinery was investigated. For such purpose, a laboratory scale rig was built. The effect of several parameters such as temperature, residence time, effect of Leca (filling-in percentage of the reactor by Leca) and UV radiation on COD removal efficiency were experimentally examined. Maximum COD removal efficiency was obtained to be 94% after final testing. An artificial neural network (ANN) was applied to evaluate the effect of operational parameters on the efficiency as the next step. A two-layered ANN provided the best results, using Levenberg–Marquardt back propagation learning algorithm (trainLM) in which tansig and purelin used as transfer functions in the hidden and output layers. Furthermore, the application of three neurons in the hidden layer caused to gratify network training while overfitting was hindered. ANN model, provided a good estimation for correlation coefficient and the mean square error (MSE) which calculated 0.997 and 0.5 × 10-3 respectively.
https://jchpe.ut.ac.ir/article_5587_a72fcea6c1ea0512df860dfdb0ba64df.pdf
2014-06-01
69
79
10.22059/jchpe.2014.5587
Wastewater treatment
COD Removal
Activated Sludge
Artificial Neural Network
Yasser
Vasseghian
y_vasseghian@yahoo.com
1
Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
LEAD_AUTHOR
Mojtaba
Ahmadi
2
Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
AUTHOR
Fazel
Dolati
3
Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran
AUTHOR
Aliakbar
Heydari
4
Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran
AUTHOR
[1] Bosch, H., Klcerebezem, G.J. and Mars, P. (1976). “Activated carbon from activated sludge.”Water Pou.Cont Fed, Vol. 48, pp. 551-651.
1
[2] Zittwitz, M., Gerhardt, M. and Ringpfeil, M. “Practical experience from commercical in-Situ bioremediation in cases of cable insulating oil and Tri- / perchloroethyleneBioprct GmbH.” RudowerChaussee 29, D-12489 Berlin, Germany.
2
[3] Tellez, G.T., Nirmalakhandan, N. and Gardea-Torresdey, J.L. (2002). “Performance evaluation of an activated sludge system for removing petroleum hydrocarbons from oil field produced water.” Adv. Environ. Res, Vol. 6, pp. 455–470.
3
[4] Freire, D.D.C., Cammarota, M.C. and Sant’Anna, G.L. (2001). “Biological treatment of oil field wastewater in a sequencing batch reactor.”Environ. Technol., Vol. 22, pp. 1125–1135.
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[5] Palmer, L.L., Beyer, A.H. and Stock, J. (1981). “Biological oxidation of dissolved compounds in oilfield produced water by a field pilot biodisk.” J. Petrol. Technol., Vol. 33, pp. 1136–1140.
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[6] Zhao, X., Wang, Y., Ye, Z., Borthwick, A.G.L. and Ni, J. (2006). “Oil field wastewater treatment in biological aerated filter by immobilized microorganisms.” Process Biochem., Vol. 41, pp. 1475–1483.
6
[7] Jackson, L.M. and Myers, J.E. (2003). “Design and construction of pilot wetlands for produced-water treatment.” in: SPE Annual Technical Conference and Exhibition Denver, Colorado, USA, 5–8 October.
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[8] Hommel, R.K. (1990). “Formation and physiological role of biosurfactants produced by hydrocarbon-utilizing microorganisms.”Biodegradation Vol. 1, pp. 107–119.
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[9] Gallagher, J.R. “Anaerobic biological treatment of produced water.” available at: http://www.energystorm.us/Anaerobic Biological Treatment of Produced Water-r 54822.html, (2001).
9
[10] Gurden, C. and Cramwinckel, J. (2000). “Application of reedbed technology in production water management.” in: SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Stavanger, Norway, 26–28 June.
10
[11] Al Mahruki, A. and Alloway, B. (2006). “The use of reed-bed technology for treating oil production waters in the sultanate of Oman.” in: SPE International Health, Safety & Environment Conference, Abu Dhabi, UAE, 2–4 April.
11
[12] Metcalf and Eddy Inc. (2003). Wastewater Eengineering, disposal &resue, 4th edition, McGraw-Hill Pub. Co., New York.
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[13] junkins, R., Deeny, K. and Eckoff, Th. (1983). The Activated Sludge Process: fundamentals of operation, Ann Arbor, Mich.Science.
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[14] Lenore, S. C., Arnold, E.G. and Andrew, D.E. (1999). Standard Methods for the Examination of Water and Wastewater, 20th edition, American Public Health Association, Washington, D.C, USA.
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[15] Fausett, L. (1994). Fundamentals of Neural Network, New Jersey, Prentice Hall.
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[16] Aleboyeh, A., Kasiri, M. B., Olya, M. E. and Aleboyeh, H. (2008).“Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks.”Dyes and Pigments, Vol. 77, pp. 288–294.
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[17] Lim, F. (1994). Neural Networks in Computer Intelligence, McGraw-Hill International Series in Computer Science.
17