ORIGINAL_ARTICLE
Simulation Study of Salinity Effect on Polymer Flooding in Core Scale
In this study, simulation of low salinity polymer flooding in the core scale is investigated using Eclipse-100 simulator. For this purpose, two sets of data are used. The first set of data were adopted from the results of experimental studies conducted at the University of Bergen, performed using Berea sandstone and intermediate oil. The second data set, related to sand pack and heavy oil system, was obtained from experiments performed at Sahand Oil and Gas Research Institute. To obtain relative permeability and capillary pressure curves, automatic history matching is implemented by coupling Eclipse-100 and MATLAB software. Three different correlations are used for relative permeability. The parameters of each model are calculated using four different optimization algorithms, including Levenberg-Marquardt, Trust-region, Fminsearch, and Pattern search. The results showed that regardless of the optimization algorithm being used, applying relative permeability model of Lomeland et al., known as LET model, best matches the experimental oil recovery data in comparison with those of Corey and Skjeaveland et al.’s relative permeability correlations. The LET model and the Trust-region algorithm were selected for simulation of low salinity polymer flooding process. Simulation of the first set of data showed that using low salinity water flooding before polymer flooding, oil recovery was increased about 16%. In addition, using the second set of data, simulation of low salinity polymer flooding scenario is investigated in a long core model, taken from one of the southwestern fields of Iran. Simulation results show an increase of about 34% in the recovery of low salinity polymer flooding compared to the water flooding scenario.
https://jchpe.ut.ac.ir/article_72597_41ce9d1c6ee114c382cdbd508ab20780.pdf
2019-12-01
137
152
10.22059/jchpe.2019.256123.1231
Eclipse-100 Simulator
History Matching
Low Salinity Polymer Injection
MATLAB Software
Optimization Algorithm
Relative Permeability
Saeideh
Mohammadi
saeidehmohammadi37@yahoo.com
1
Faculty of Petroleum and Natural Gas Engineering, Sahand Oil and Gas Research Institute (SOGRI), Sahand University of Technology, Sahand New Town, Tabriz, Iran
AUTHOR
Elnaz
Khodapanah
khodapanah@sut.ac.ir
2
Faculty of Petroleum and Natural Gas Engineering, Sahand Oil and Gas Research Institute (SOGRI), Sahand University of Technology, Sahand New Town, Tabriz, Iran
LEAD_AUTHOR
Seyyed Alireza
Tabatabaei-Nejad
tabatabaei@sut.ac.ir
3
Faculty of Petroleum and Natural Gas Engineering, Sahand Oil and Gas Research Institute (SOGRI), Sahand University of Technology, Sahand New Town, Tabriz, Iran
AUTHOR
[1] Shiran BS. Enhanced Oil Recovery by Combined Low Salinity Water and Polymer Flooding. [Ph.D. dissertation]. Bergen: University of Bergen; 2014.
1
[2] Ayirala SC, Uehara-Nagamine E, Matzakos AN, Chin RW, Doe PH, van den Hoek PJ. A designer water process for offshore low salinity and polymer flooding applications. InSPE Improved Oil Recovery Symposium 2010 Jan 1.
2
[3] Vermolen EC, Pingo-Almada M, Wassing BM, Ligthelm DJ, Masalmeh SK. Low-salinity polymer flooding: improving polymer flooding technical feasibility and economics by using low-salinity make-up brine. International Petroleum Technology Conference. 2014 Jan 19.
3
[4] Mohammadi H, Jerauld G. Mechanistic modeling of the benefit of combining polymer with low salinity water for enhanced oil recovery. InSPE Improved Oil Recovery Symposium 2012 Jan 1.
4
[5] Algharaib M, Alajmi A, Gharbi R. Improving polymer flood performance in high salinity reservoirs. Journal of Petroleum Science and Engineering. 2014 Mar 1;115:17-23.
5
[6] Chandrashegaran P. Low Salinity Water Injection for EOR. InSPE Nigeria Annual International Conference and Exhibition 2015 Aug 4.
6
[7] Al-Sawafi MS. Simulation of enhanced heavy oil recovery: history match of waterflooding and polymer injection at adverse mobility ratio. [Master's thesis]. Bergen: University of Bergen; 2015.
7
[8] Torrijos ID, Puntervold T, Strand S, Austad T, Bleivik TH, Abdullah HI. An experimental study of the low salinity Smart Water-Polymer hybrid EOR effect in sandstone material. Journal of Petroleum Science and Engineering. 2018 May 1;164:219-29.
8
[9] Unsal E, Ten Berge AB, Wever DA. Low salinity polymer flooding: Lower polymer retention and improved injectivity. Journal of Petroleum Science and Engineering. 2018 Apr 1;163:671-82.
9
[10] Shaker Shiran B, Skauge A. Enhanced oil recovery (EOR) by combined low salinity water/polymer flooding. Energy & Fuels. 2013 Mar 6;27(3):1223-35.
10
[11] Shaker Shiran B, Skauge A. Wettability and oil recovery by low salinity injection. InSPE EOR Conference at Oil and Gas West Asia 2012 Jan 1.
11
[12] Basbug B. Analysis of spontaneous imbibition in fractured, heterogeneous sandstone. [Doctoral dissertation]. State College: Penn State University; 2008.
12
[13] Lee CH. Parametric study of factors affecting capillary imbibition in fractured porous media. [Doctoral dissertation]. State College: Penn State University; 2010.
13
[14] Sayyafzadeh M. Uncertainty reduction in reservoir characterisation through inverse modelling of dynamic data: an evolutionary computation approach. [Doctoral dissertation]. Adelaide: University of Adelaide; 2013.
14
[15] Xu S. Automatic history match and upscaling study of VAPEX process and its uncertainty analysis. [Doctoral dissertation]. Regina: University of Regina; 2012.
15
[16] Sun X, Mohanty KK. Estimation of flow functions during drainage using genetic algorithm. SPE Journal. 2005 Dec 1;10(04):449-57.
16
[17] Corey AT. The interrelation between gas and oil relative permeabilities. Producers monthly. 1954 Nov;19(1):38-41.
17
[18] Skjaeveland SM, Siqveland LM, Kjosavik A, Hammervold WL, Virnovsky GA. Capillary pressure correlation for mixed-wet reservoirs. InSPE India Oil and Gas Conference and Exhibition 1998 Jan 1.
18
[19] Burdine N. Relative permeability calculations from pore size distribution data. Journal of Petroleum Technology. 1953 Mar;5(3):71-8.
19
[20] Standing MB. Notes on relative permeability relationships. unpublished report, Division of Petroleum Engineering and Applied Geophysics, The Norwegian Institute of Technology, The University of Trondheim. 1974 Aug.
20
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21
ORIGINAL_ARTICLE
Investigation of Asphaltene Precipitation Using Response Surface Methodology Combined with Artificial Neural Network
The precipitation of asphaltene, one of the components of oil, in reservoirs, transfer lines, and equipment causes many problems. Accordingly, researchers are prompted to determine the factors affecting asphaltene precipitation and methods of avoiding its formation. Predicting precipitation and examining the simultaneous effect of operational variables on asphaltene precipitation are difficult because of the multiplicity, complexity, and nonlinearity of factors affecting asphaltene precipitation and the high cost of experiments. This study combined the use of response surface methodology and the artificial neural network to predict asphaltene precipitation under the mutual effects of various parameters. The values of such parameters were determined to reach the minimum amount of precipitation. We initially selected the appropriate algorithm for predicting asphaltene precipitation from the two neural network algorithms. The outputs of designed experiments in response surface methodology were determined using the optimum algorithm of the neural network. The effects of variables on asphaltene precipitation were then investigated by response surface methodology. According to the results, the minimum precipitation of asphaltene achieved at zero mole percent of injected nitrogen and methane, 10–20 mole percent of injected carbon dioxide, asphaltene content of 0.46, the resin content of 16.8 weight percent, the pressure of 333 psi, and temperature of 180 . Results showed that despite the complexities of asphaltene precipitation, the combination of artificial neural network with response surface methodology can be successfully used to investigate the mutual effect of different variables affecting asphaltene precipitation.
https://jchpe.ut.ac.ir/article_73622_b47dfb525580493582e03d6a5867484a.pdf
2019-12-01
153
167
10.22059/jchpe.2019.261438.1238
Artificial Neural Network Asphaltene
desirability
Precipitation
response surface methodology
Zeinab
Hosseini-dastgerdi
zeinab.hosseini@uut.ac.ir
1
Faculty of Chemical Engineering, Urmia University of Technology, Urmia, Iran
AUTHOR
Saeid
Jafarzadeh-Ghoushchi
s.jafarzadeh@uut.ac.ir
2
Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
LEAD_AUTHOR
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1
[2] Mullins OC, Sabbah H, Eyssautier J, Pomerantz AE, Barré L, Andrews AB, Ruiz-Morales Y, Mostowfi F, McFarlane R, Goual L, Lepkowicz R. Advances in asphaltene science and the Yen–Mullins model. Energy & Fuels. 2012 May 8;26(7):3986-4003.
2
[3] Hosseini-Dastgerdi Z, Meshkat SS. An experimental and modeling study of asphaltene adsorption by carbon nanotubes from model oil solution. Journal of Petroleum Science and Engineering. 2019 Mar 1;174:1053-61.
3
[4] Dastgerdi ZH, Meshkat SS, Hosseinzadeh S, Esrafili MD. Application of Novel Fe3O4–Polyaniline Nanocomposites in Asphaltene Adsorptive Removal: Equilibrium, Kinetic Study and DFT Calculations. Journal of Inorganic and Organometallic Polymers and Materials. 2019 Jul 1;29(4):1160-70.
4
[5] Hosseini‐Dastgerdi Z, Tabatabaei‐Nejad SA, Khodapanah E, Sahraei E. A comprehensive study on mechanism of formation and techniques to diagnose asphaltene structure; molecular and aggregates: a review. Asia‐Pacific Journal of Chemical Engineering. 2015 Jan;10(1):1-14.
5
[6] Hosseini-Dastgerdi Z, Tabatabaei-Nejad SA, Sahraei E, Nowroozi H. Morphology and size distribution characterization of precipitated asphaltene from live oil during pressure depletion. Journal of Dispersion Science and Technology. 2015 Mar 4;36(3):363-8.
6
[7] Akbarzadeh K, Hammami A, Kharrat A, Zhang D, Allenson S, Creek J, Kabir S, Jamaluddin A, Marshall AG, Rodgers RP, Mullins OC. Asphaltenes—problematic but rich in potential. Oilfield Review. 2007 Jul;19(2):22-43.
7
[8] Tavakkoli M, Panuganti SR, Taghikhani V, Pishvaie MR, Chapman WG. Precipitated asphaltene amount at high-pressure and high-temperature conditions. Energy & Fuels. 2013 Nov 20;28(3):1596-610.
8
[9] Zanganeh P, Ayatollahi S, Alamdari A, Zolghadr A, Dashti H, Kord S. Asphaltene deposition during CO2 injection and pressure depletion: a visual study. Energy & Fuels. 2012 Jan 13;26(2):1412-9.
9
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[11] AlHammadi AA, Chen Y, Yen A, Wang J, Creek JL, Vargas FM, Chapman WG. Effect of the gas composition and gas/oil ratio on asphaltene deposition. Energy & Fuels. 2017 Mar 10;31(4):3610-9.
11
[12] Zanganeh P, Dashti H, Ayatollahi S. Visual investigation and modeling of asphaltene precipitation and deposition during CO2 miscible injection into oil reservoirs. Fuel. 2015 Nov 15;160:132-9.
12
[13] Wang P, Zhao F, Hou J, Lu G, Zhang M, Wang Z. Comparative Analysis of CO2, N2, and Gas Mixture Injection on Asphaltene Deposition Pressure in Reservoir Conditions. Energies. 2018 Sep;11(9):2483-97
13
[14] Mullins OC. The modified Yen model. Energy & Fuels. 2010 Jan 19;24(4):2179-207.
14
[15] Anisimov MA, Ganeeva YM, Gorodetskii EE, Deshabo VA, Kosov VI, Kuryakov VN, Yudin DI, Yudin IK. Effects of resins on aggregation and stability of asphaltenes. Energy & Fuels. 2014 Sep 24;28(10):6200-9.
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[16] Li Z, Firoozabadi A. Cubic-plus-association equation of state for asphaltene precipitation in live oils. Energy & fuels. 2010 Apr 5;24(5):2956-63.
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[17] Dehaghani YH, Assareh M, Feyzi F. Asphaltene precipitation modeling with PR and PC-SAFT equations of state based on normal alkanes titration data in a Multisolid approach. Fluid Phase Equilibria. 2018 Aug 25;470:212-20.
17
[18] Merino‐Garcia D, Correra S. A shortcut application of a Flory‐like model to Asphaltene precipitation. Journal of Dispersion Science and Technology. 2007 Mar 1;28(3):339-47.
18
[19] Manshad AK, Edalat M. Application of continuous polydisperse molecular thermodynamics for modeling asphaltene precipitation in crude oil systems. Energy & Fuels. 2008 May 22;22(4):2678-86.
19
[20] Arya A, Liang X, Von Solms N, Kontogeorgis GM. Modeling of asphaltene onset precipitation conditions with cubic plus association (CPA) and perturbed chain statistical associating fluid theory (PC-SAFT) equations of state. Energy & Fuels. 2016 Jul 26;30(8):6835-52.
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[21] Gharagheizi F, Eslamimanesh A, Sattari M, Mohammadi AH, Richon D. Corresponding states method for evaluation of the solubility parameters of chemical compounds. Industrial & Engineering Chemistry Research. 2012 Feb 22;51(9):3826-31.
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[22] Pazuki GR, Nikookar M. A modified Flory-Huggins model for prediction of asphaltenes precipitation in crude oil. Fuel. 2006 May 1;85(7-8):1083-6.
22
[23] Yazdizadeh M, Nourbakhsh H, Jafari Nasr MR. A Solution Model for Predicting Asphaltene Precipitation. Iranian Journal of Chemistry and Chemical Engineering (IJCCE). 2014 Mar 1;33(1):93-102.
23
[24] Ahmadi M, Jafarzadeh-Ghoushchi S, Taghizadeh R, Sharifi A. Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches. Neural Computing and Applications.:1-20.
24
[25] Salem S, Jafarzadeh-Ghoushchi S. Estimation of optimal physico-chemical characteristics of nano-sized inorganic blue pigment by combined artificial neural network and response surface methodology. Chemometrics and Intelligent Laboratory Systems. 2016 Dec 15;159:80-8.
25
[26] Jafarzadeh-Ghoushchi S, Rahman MN. Performance study of artificial neural network modelling to predict carried weight in the transportation system. International Journal of Logistics Systems and Management. 2016;24(2):200-12.
26
[27] Al Ameen A, Mondal S, Pudi SM, Pandhare NN, Biswas P. Liquid phase hydrogenolysis of glycerol over highly active 50% Cu-Zn (8: 2)/MgO catalyst: reaction parameter optimization by using response surface methodology. Energy & Fuels. 2017;31(8):8521-33.
27
[28] Ahmadi MA, Golshadi M. Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. Journal of Petroleum Science and Engineering. 2012 Nov 1;98:40-9.
28
[29] Abedini A, Ashoori S, Torabi F, Saki Y, Dinarvand N. Mechanism of the reversibility of asphaltene precipitation in crude oil. Journal of Petroleum Science and Engineering. 2011 Aug 1;78(2):316-20.
29
[30] Khamehchi E, Behvandi R. Prediction of Bubble Point Pressure & Asphaltene Onset Pressure During CO2 Injection Using ANN & ANFIS Models. Journal of Petroleum Science and Technology. 2011 Aug 10;1(2):35-45.
30
[31] Ashoori S, Abedini A, Abedini R, Nasheghi KQ. Comparison of scaling equation with neural network model for prediction of asphaltene precipitation. Journal of Petroleum Science and Engineering. 2010 May 1;72(1-2):186-94.
31
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32
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33
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34
[35] Afshari S, Kharrat R, Ghazanfari MH. Asphaltene precipitation study during natural depletion at reservoir conditions. InInternational Oil and Gas Conference and Exhibition in China 2010 Jan 1. Society of Petroleum Engineers.
35
[36] Moradi S, Dabiri M, Dabir B, Rashtchian D, Emadi MA. Investigation of asphaltene precipitation in miscible gas injection processes: experimental study and modeling. Brazilian Journal of Chemical Engineering. 2012 Sep;29(3):665-76.
36
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37
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39
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40
[41] Jafarzadeh SG, Rahman MN, Wahab DA. Forecasting capabilities of spare part production with artificial neural networks model in a supply chain. World Applied Sciences Journal. 2012;20(5):674-8.
41
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42
[43] Ahmadi MA. Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. Journal of Petroleum Exploration and Production Technology. 2011 Dec 1;1(2-4):99-106.
43
[44] Khayet M, Cojocaru C, García-Payo C. Application of response surface methodology and experimental design in direct contact membrane distillation. Industrial & engineering chemistry research. 2007 Aug 15;46(17):5673-85.
44
[45] Mohammadi M, Dadvar M, Dabir B. Application of response surface methodology for optimization of the stability of asphaltene particles in crude oil by TiO2/SiO2 nanofluids under static and dynamic conditions. Journal of Dispersion Science and Technology. 2018 Mar 4;39(3):431-42.
45
ORIGINAL_ARTICLE
Lactic-based Novel Amine Ionic Liquid: Synthesis and Characterization of [DEA][Lac]
In this study, a novel amine ionic liquid “Diethanolamine Lactic” [DEA][Lac] was synthesized. The replacement of halogenated ion fluid was used as a modified methodology for the preparation of diethanolamine based on lactic acid. The ionic liquid was characterized using the Fourier transform infrared (FTIR) spectra and the nuclear magnetic resonance (NMR) spectroscopy. The changes in bands' wavelengths and the peaks of the participating elements and process materials have been confirmed before and after synthesis based on the results of FTIR analysis which demonstrate the successful synthesis of diethanolamine lactic. The NMR analysis also clearly confirms the synthesis of diethanolamine lactic acid. Analysis results were shown in the successful synthesis of the [diethanolamine] [lactic].
https://jchpe.ut.ac.ir/article_72683_47722d271a25305b787b6a563900f3b4.pdf
2019-12-01
169
176
10.22059/jchpe.2019.264208.1243
diethanolamine
HCl
Ionic liquids
lactic acid
synthesis
Mohsen
Samimi
m.samimi@hotmail.com
1
Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran
LEAD_AUTHOR
Ali
Hojatnia
a.hojatnia@yahoo.com
2
Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran
AUTHOR
[1] Luo QX, An BW, Ji M, Zhang J. Hybridization of metal–organic frameworks and task-specific ionic liquids: fundamentals and challenges. Materials Chemistry Frontiers. 2018;2(2):219-34.
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[2] Wang LY, Xu YL, Li ZD, Wei YN, Wei JP. CO2/CH4 and H2S/CO2 selectivity by ionic liquids in natural gas sweetening. Energy & fuels. 2017 Dec 22;32(1):10-23.
2
[3] Wei Z, Zhang ZH, Wang MM, Xu L, Liu B, Jiao H. Combination effect of ligands and ionic liquid components on the structure and properties of manganese metal–organic frameworks. CrystEngComm. 2017;19(36):5402-11.
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[4] Kinik FP, Uzun A, Keskin S. Ionic liquid/metal–organic framework composites: from synthesis to applications. ChemSusChem. 2017 Jul 21;10(14):2842-63.
4
[5] Wang B, Qin L, Mu T, Xue Z, Gao G. Are ionic liquids chemically stable?. Chemical reviews. 2017 Feb 27;117(10):7113-31.
5
[6] Cui G, Wang J, Zhang S. Active chemisorption sites in functionalized ionic liquids for carbon capture. Chemical Society Reviews. 2016;45(15):4307-39.
6
[7] Basile A, Bhatt AI, O’Mullane AP. Stabilizing lithium metal using ionic liquids for long-lived batteries. Nature communications. 2016 Jun 13;7:ncomms11794.
7
[8] Forsyth M, Girard GM, Basile A, Hilder M, MacFarlane DR, Chen F, Howlett PC. Inorganic-organic ionic liquid electrolytes enabling high energy-density metal electrodes for energy storage. Electrochimica Acta. 2016 Dec 1;220:609-17.
8
[9] Ohno H, editor. Electrochemical aspects of ionic liquids. John Wiley & Sons; 2005 Aug 8.
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[11] Ye C, Liu W, Chen Y, Yu L. Room-temperature ionic liquids: a novel versatile lubricant. Chemical Communications. 2001(21):2244-5.
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[12] Wang Y, Yang H. Synthesis of CoPt nanorods in ionic liquids. Journal of the American Chemical Society. 2005 Apr 20;127(15):5316-7.
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[13] Ding K, Miao Z, Liu Z, Zhang Z, Han B, An G, Miao S, Xie Y. Facile synthesis of high quality TiO2 nanocrystals in ionic liquid via a microwave-assisted process. Journal of the American Chemical Society. 2007 May 23;129(20):6362-3.
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[14] Zhao D, Wu M, Kou Y, Min E. Ionic liquids: applications in catalysis. Catalysis today. 2002 May 15;74(1-2):157-89.
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[15] Geldbach TJ, Dyson PJ. A versatile ruthenium precursor for biphasic catalysis and its application in ionic liquid biphasic transfer hydrogenation: conventional vs task-specific catalysts. Journal of the American Chemical Society. 2004 Jul 7;126(26):8114-5.
15
[16] Mora‐Pale M, Meli L, Doherty TV, Linhardt RJ, Dordick JS. Room temperature ionic liquids as emerging solvents for the pretreatment of lignocellulosic biomass. Biotechnology and bioengineering. 2011 Jun;108(6):1229-45.
16
[17] Ouellet M, Datta S, Dibble DC, Tamrakar PR, Benke PI, Li C, Singh S, Sale KL, Adams PD, Keasling JD, Simmons BA. Impact of ionic liquid pretreated plant biomass on Saccharomyces cerevisiae growth and biofuel production. Green Chemistry. 2011;13(10):2743-9.
17
[18] Azizi D, Larachi F. Immiscible dual ionic liquid-ionic liquid mineral separation of rare-earth minerals. Separation and Purification Technology. 2018 Jan 31;191:340-53.
18
[19] Karpińska M, Wlazło M, Domańska U. Investigation on the ethylbenzene/styrene separation efficiency with ionic liquids in liquid–liquid extraction. Chemical Engineering Research and Design. 2017 Dec 1;128:214-20.
19
[20] Mashuga M, Olasunkanmi L, Adekunle A, Yesudass S, Kabanda M, Ebenso E. Adsorption, thermodynamic and quantum chemical studies of 1-hexyl-3-methylimidazolium based ionic liquids as corrosion inhibitors for mild steel in HCl. Materials. 2015 Jun 17;8(6):3607-32.
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[21] Hanza AP, Naderi R, Kowsari E, Sayebani M. Corrosion behavior of mild steel in H2SO4 solution with 1, 4-di [1′-methylene-3′-methyl imidazolium bromide]-benzene as an ionic liquid. Corrosion Science. 2016 Jun 1;107:96-106.
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[22] Likhanova NV, Domínguez-Aguilar MA, Olivares-Xometl O, Nava-Entzana N, Arce E, Dorantes H. The effect of ionic liquids with imidazolium and pyridinium cations on the corrosion inhibition of mild steel in acidic environment. Corrosion Science. 2010 Jun 1;52(6):2088-97.
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[23] Kodama K, Tsuda R, Niitsuma K, Tamura T, Ueki T, Kokubo H, Watanabe M. Structural effects of polyethers and ionic liquids in their binary mixtures on lower critical solution temperature liquid-liquid phase separation. Polymer journal. 2011 Mar;43(3):242.
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[24] Sun J, Macfarlane DR, Forsyth M. A new family of ionic liquids based on the 1-alkyl-2-methyl pyrrolinium cation. Electrochimica acta. 2003 May 30;48(12):1707-11.
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[30] Zhang Z, Zhang Q, Zhang Q, Zhang T, Li W. Isobaric vapor–liquid equilibrium of tert-butyl alcohol + water + triethanolamine-based ionic liquid ternary systems at 101.3 kPa. Journal of Chemical & Engineering Data. 2015 Jun 9;60(7):2018-27.
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[35] Lv B, Xia Y, Shi Y, Liu N, Li W, Li S. A novel hydrophilic amino acid ionic liquid [C2OHmim][Gly] as aqueous sorbent for CO2 capture. International Journal of Greenhouse Gas Control. 2016 Mar 1;46:1-6.
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36
ORIGINAL_ARTICLE
Bubble Pressure Prediction of Reservoir Fluids using Artificial Neural Network and Support Vector Machine
Bubble point pressure is an important parameter in equilibrium calculations of reservoir fluids and having other applications in reservoir engineering. In this work, an artificial neural network (ANN) and a least square support vector machine (LS-SVM) have been used to predict the bubble point pressure of reservoir fluids. Also, the accuracy of the models have been compared to two-equation state-based models, i.e. SRK-EOS and PR-EOS and four empirical equations, i.e. Whitson, Standing, Wilson and Ghafoori et al. Compared to the experimental data, the average relative deviations (ARD) of bubble pressure prediction for these equations were obtained to be 14%, 29%, 66%, 30%, 38%, and 11%, respectively. The best semi-empirical equation has an ARD of about 11% while, the ANN and LS-SVM models have an ARD of 8% and 4.68%, respectively. Thus, it can be concluded that generally, these soft computing models appear to be more accurate than the empirical and EOS based methods for prediction of bubble point pressure of reservoir fluids.
https://jchpe.ut.ac.ir/article_72598_e1b9d56d658e9c1fff47a374e9daf938.pdf
2019-12-01
177
189
10.22059/jchpe.2019.266793.1251
Artificial Neural Network
Bubble pressure
empirical correlations
Genetic Algorithm
reservoir fluids
Support vector machine
Afshin
Dehghani Kiadehi
afshin_1982@ymail.com
1
Faculty of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
AUTHOR
Bahman
Mehdizadeh
bahmanmehdizadeh@gmail.com
2
Faculty of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
AUTHOR
kamyar
Movagharnejad
k-movaghar@nit.ac.ir
3
Faculty of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
LEAD_AUTHOR
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[18] Aghaeinejad-Meybodi A, Ebadi A, Shafiei S, Khataee A, Kiadehi AD. Degradation of Fluoxetine using catalytic ozonation in aqueous media in the presence of nano-γ-alumina catalyst: Experimental, modeling and optimization study. Separation and Purification Technology. 2019 Mar 18;211:551-63.
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31
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32
ORIGINAL_ARTICLE
PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent
In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (SA), temperature (T), and pressure (P) were related to the output parameter which is propylene or propane adsorption. A thorough comparison between the experimental, artificial neural network and particle swarm optimization-adaptive neuro-fuzzy inference system models was carried out to prove its efficiency in accurate prediction and computation time. The obtained results show that both investigated methods have good agreements in comparison with the experimental data, but the proposed artificial neural network structure is more precise than our proposed PSO-ANFIS structure. Mean absolute error (MAE) for ANN and ANFIS models were 0.111 and 0.421, respectively.
https://jchpe.ut.ac.ir/article_72487_9072cfbb693845980dbef5eaa99ba3c8.pdf
2019-12-01
191
201
10.22059/jchpe.2019.269113.1256
Adsorption
ANN
Cu-BTC
Propylene/Propane
PSO-ANFIS
Sohrab
Fathi
sohrab.fathi@gmail.com
1
Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran
AUTHOR
Abbas
Rezaei
unrezaei@yahoo.com
2
Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah, Iran
AUTHOR
Majid
Mohadesi
m.mohadesi@kut.ac.ir
3
Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran
LEAD_AUTHOR
Mona
Nazari
mona.1074@yahoo.com
4
Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran
AUTHOR
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[18] Grande CA, Firpo N, Basaldella E, Rodrigues AE. Propane/propene separation by SBA-15 and π-complexated Ag-SBA-15. Adsorption. 2005 Jul 1;11(1):775-80.
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[19] Padin J, Rege SU, Yang RT, Cheng LS. Molecular sieve sorbents for kinetic separation of propane/propylene. Chemical Engineering Science. 2000 Oct 15;55(20):4525-35.
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[32] Gomes PS, Lamia N, Rodrigues AE. Design of a gas phase simulated moving bed for propane/propylene separation. Chemical Engineering Science. 2009 Mar 16;64(6):1336-57.
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[33] Da Silva FA, Rodrigues AE. Adsorption equilibria and kinetics for propylene and propane over 13X and 4A zeolite pellets. Industrial & Engineering Chemistry Research. 1999 May 3;38(5):2051-7.
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[35] Grande CA, Basaldella E, Rodrigues AE. Crystal size effect in vacuum pressure-swing adsorption for propane/propylene separation. Industrial & Engineering Chemistry Research. 2004 Nov 10;43(23):7557-65.
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[36] Merad− Dib H, Bendenia S, Merouani DR, Bendenia C, Batonneau− Gener I, Khelifa A. Adsorption of Propylene and Propane onto M n+ X (M n+= Cr3+ and/or Ni2+) Zeolites and Comparison between Binary and Ternary Exchanges. Journal of Chemical & Engineering Data. 2016 Aug 31;61(10):3510-8.
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63
ORIGINAL_ARTICLE
An Investigation on Corrosion and Stress Corrosion Cracking initiation of a Ferritic Stainless Steel in a Tertiary Amine Solution
The present study focused on stress corrosion cracking (SCC) and corrosion behavior of ferritic stainless steel (grade 430) in activated methyl diethanolamine (aMDEA) solution, which is classified as a tertiary amine. In this regard, cyclic polarization and U-bend tests were performed in CO2 loaded aMDEA with different concentrations at 25 and 70 °C to observe corrosion behavior and also the possibility of crack initiation. Based on the obtained results, it was found that the corrosion rate increased in concentrated amine solutions. Also, by increasing temperature from 25 to 70 °C both corrosion rate and susceptibility to SCC initiation were intensified. Increment of amine concentration and also increase in temperature led to more absorption of CO2, generating a more acidic solution. Overall it could be stated that while for the grade 430 stainless steel investigated in this study corrosion and cracking was observed Therefore it could be concluded that in amine-containing environments this steel is not a very suitable alternative for carbon steels, which are commonly used in these environments.
https://jchpe.ut.ac.ir/article_72627_8a70896ef04a3f82706ed73638bbf4d6.pdf
2019-12-01
203
210
10.22059/jchpe.2019.271003.1258
aMDEA
Corrosion
Ferritic Stainless-Steel Tertiary Amine Solution Stress
Corrosion Cracking
Hassan
Panahi
hasan.p2009@yahoo.com
1
Materials Engineering Department, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Abdolmajid
Eslami
m.eslami@cc.iut.ac.ir
2
Materials Engineering Department, Isfahan University of Technology, Isfahan, Iran
LEAD_AUTHOR
[1] Roger BR, inventor; Girdler Corp, assignee. Process for separating acidic gases. United States patent US 1,783,901. 1930 Dec 2.
1
[2] Soosaiprakasam IR, Veawab A. Corrosion and polarization behavior of carbon steel in MEA-based CO2 capture process. International journal of greenhouse gas control. 2008 Oct 1;2(4):553-62.
2
[3] Maddox RN, Campbell JM. Gas conditioning and processing: Gas treating and sulfur recovery. Campbell Petroleum Services; 1998.
3
[4] Mitra S. A Technical Report on Gas Sweetening by Amines. Petrofac Engineering India Ltd. 2015;1.
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[5] Veldman R. Alkanolamine solution corrosion mechanisms and inhibition from heat stable salts and CO2. InCORROSION 2000. 2000 Jan 1. NACE International.
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[6] Nielsen RB, Lewis KR, McCullough JG, Hansen DA. Controlling corrosion in amine treating plants. InProceedings of the Laurance Reid Gas Conditioning Conference, Norman, Oklahoma; 1995 Feb 26.
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[7] Cummings AL, Veatch FC, Keller AE. Corrosion and corrosion control methods in amine systems containing H2S. InCorrosion97 1997 Jan 1. NACE International.
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[8] Khorrami MR, Raeissi K, Shahban H, Torkan MA, Saatchi A. Corrosion behavior of carbon steel in carbon dioxide-loaded activated methyl diethanol amine solution. Corrosion. 2008 Feb;64(2):124-30.
8
[9] Krzemień A, Więckol-Ryk A, Smoliński A, Koteras A, Więcław-Solny L. Assessing the risk of corrosion in amine-based CO2 capture process. Journal of Loss Prevention in the Process Industries. 2016 Sep 1;43:189-97.
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[10] Rooney PC, DuPart M. Corrosion in alkanolamine plants: causes and minimization. InCORROSION 2000 2000 Jan 1. NACE International.
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[11] Fontana MG. Corrosion engineering. Tata McGraw-Hill Education; 2005.
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[13] Namazi F, Almasi H. Amine corrosion in gas sweetening plant: causes and minimization on real case study. InNACE International Corrosion Conference Proceedings. 2016. NACE International.
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[16] Panahi H, Eslami A, Golozar MA. Corrosion and stress corrosion cracking initiation of grade 304 and 316 stainless steels in activated Methyl Diethanol Amine (aMDEA) solution. Journal of Natural Gas Science and Engineering. 2018 Jul 1;55:106-12.
16
[17] Tao X, Li C, Han L, Gu J. Microstructure evolution and mechanical properties of X12CrMoWVNbN10-1-1 steel during quenching and tempering process. Journal of Materials Research and Technology. 2016 Jan 1;5(1):45-57.
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[18] Hartono A, Saeed M, Kim I, Svendsen HF. Protonation constant (pKa) of MDEA in water as function of temperature and ionic strength. Energy Procedia. 2014 Jan 1;63:1122-8.
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[19] Kaesche H. Corrosion of metals: physicochemical principles and current problems. Springer Science & Business Media; 2012 Dec 6.
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[20] Charbonnier JC, Noual P. The influence of molybdenum on the behaviour of 17Cr pure ferritic steels in a 20% HCOOH medium at 70 ° C. Corrosion Science. 1977 Jan 1;17(12):1009-14.
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[21] Panahi H, Eslami A, Golozar MA. Corrosion and stress corrosion cracking initiation of grade 304 and 316 stainless steels in activated Methyl Diethanol Amine (aMDEA) solution. Journal of Natural Gas Science and Engineering. 2018 Jul 1;55:106-12.
21
[22] Kohl AL, Nielsen R. Gas purification. Elsevier; 1997 Aug 28.
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[23] Jones DA. Principles and prevention of corrosion. 2nd edition. vol. 5. New Jersey: Prentice Hall; 1996.
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[24] Revie RW. Corrosion and corrosion control: an introduction to corrosion science and engineering. John Wiley & Sons; 2008 May 16.
24
ORIGINAL_ARTICLE
Analysis of the Casing Collapse in Terms of Geomechanical Parameters and Solid Mechanics
Casing collapse is one of the major problems in oil fields, imposing a lot of costs on oil companies. This problem occurs not only at drilling times in some formations but also after the completion and production can lead to many problems. Analysis of the behavior of casing collapse in terms of geo-mechanics and solid mechanics could significantly meet the needs of the oil industry of Iran. In this study, at first, casing collapse behavior is investigated by considering the formation creep and casing production defects using numerical methods. Then, the effect of some solid mechanics parameters on the casing collapse is investigated. The results showed that casing construction defects, such as ovality and eccentricity and residual stress, could greatly reduce the casing collapse resistance. The resistance reduction of the casing is about 30.37, 9.65, and 46.87 percent respectively, so that when the casing is placed into the well, it undergoes high strain and finally could be reached to collapse. In addition, it was found that the construction defects show a higher effect on casing collapse than the salt rock creep.
https://jchpe.ut.ac.ir/article_72531_4bb848a083796dc073ccedec150d68c4.pdf
2019-12-01
211
225
10.22059/jchpe.2019.274240.1267
Casing Collapse
Casing Production Defects
Salt Rock Creep
Geomechanics Numerical Modeling
Farid
Ghodusi
f.ghodusi@jdeihe.ac.ir
1
ACECR Institute of Higher Education, Isfahan Branch, Petroleum Engineering Department, Isfahan, Iran
AUTHOR
Hossein
Jalalifar
jalalifar@uk.ac.ir
2
Petroleum Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
Saeed
Jafari
jafari@uk.ac.ir
3
Petroleum Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
[1] Smith MB, Pattillo PD. Analysis of casing deformations due to formation flow. InApplied Oilsands Geoscience Conference; 1980 Jan 15.
1
[2] Oliveira JE, Idagawa LS, Nogueira EC. Evaporite in Campos Basin: Geological Aspects and Drilling Problems. Report Cenpes-475. 1985.
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[3] Dusseault MB, Maury V, Sanfilippo F, Santarelli FJ. Drilling through salt: constitutive behavior and drilling strategies. InGulf Rocks 2004, the 6th North America Rock Mechanics Symposium (NARMS) 2004 Jan 1.
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[4] ICOFC, Linear tube and casing", Iranian Central Oil Fields Co, Tehran. 2014.
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[5] Jamedari R. Collapse capacity for a pipeline with thick coating. [Master's thesis]. Ås: Norwegian University of Life Sciences; 2015.
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[6] Huang X, Mihsein M. Finite element prediction of the ultimate collapse strength of casings. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2000 Dec 1;214(12):1515-27.
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[7] Cheatham Jr JB, McEver JW. Behavior of casing subjected to salt loading. Journal of Petroleum Technology. 1964 Sep 1;16(09):1069-75.
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[8] Zavrakesh M, and Kazemi A. The Change of properties of drilling cement slurry using nanomaterial as an effective strategy for preventing the casing collapse. National Conference on Landscape 1404, and Technological Achievements in Engineering Sciences. 2015. (in Persian)
8
[9] Willson SM, Fossum AF, Fredrich JT. Assessment of salt loading on well casings. InIADC/SPE Drilling Conference 2002 Jan 1.
9
[10] Mohebi, R. and Jalalifar, H. "Analysis of the effect of cement on the casing stability in salt formations", Third National Conference on Oil and Gas and Related Industries. 2015. (in Persian)
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[11] Nasehi M, Kamali MT, Nasehi S. Analysis of stress in casings covered with cement in oil and gas wells using the finite element method. Third National Congress of Petroleum Engineering. 2011. (in Persian)
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[12] Clinedinst WO. A rational expression for the critical collapsing pressure of pipe under external pressure. InDrilling and production practice. 1939 Jan 1.
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[13] Tamano T, Mimaki T, Yanagimoto S. A new empirical formula for collapse resistance of commercial casing. Nippon Steel Tech. Rep.. 1985 Jul(26):19-26.
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[14] Tokimasa K, Tanaka K. FEM Analysis of the Collapse Strength of a Tube. Journal of Pressure Vessel Technology. 1986 May 1;108(2):158-64.
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[15] Issa JA, Crawford DS. An improved design equation for tubular collapse. InSPE Annual Technical Conference and Exhibition. 1993 Jan 1.
15
[16] Nasehi M, Kamali MT, Nasehi S. Buckling analysis of casing using finite element method. Third National Congress of Petroleum Engineering. 2011. (in Persian)
16
[17] ICOFC Drilling Program (Dehloran -28 oil well), Dehloran, Iran. 2012.
17
[18] Bruno MS. Geomechanical and decision analyses for mitigating compaction-related casing damage. SPE drilling & completion. 2002 Sep 1;17(03):179-88.
18
ORIGINAL_ARTICLE
Investigation of Thermodynamic Consistency Test of Carbon Dioxide (CO2) in Room-Temperature Ionic liquids using Generic van der Waals Equation of State
Thermodynamic consistency test of isothermal vapor-liquid equilibrium (VLE) data of various binary systems containing Carbon dioxide (CO2)/Room temperature ionic liquids (RTILs) have been investigated in wide ranges of pressures in each isotherm precisely. In this paper Generic van der Waals (GvdW) equation of state (EoS) coupled with modified van der Waals Berthelot mixing rule has successfully been applied for correlating P-T-x binary data. The optimum parameters were obtained by minimizing the average relative deviation between modeled and experimental data based on the bubble pressure algorithm. Modeling is highly shown satisfactory in all cases which means that deviations in correlated data are low subsequently can prove that the flexibility and capability of the proposed model for thermodynamic consistency. Results of the consistency test represented ten isothermal experimental data set to be thermodynamically consistent, fourteen were declared to be not fully consistent and just four isothermal experimental data sets were represented to be thermodynamically inconsistent.
https://jchpe.ut.ac.ir/article_72625_88c8c2f4bcc56e5415835b43fdc74788.pdf
2019-12-01
227
236
10.22059/jchpe.2019.276422.1269
carbon dioxide
Equation of State
Generic Van Der Waals
Room-Temperature Ionic Liquids
Thermodynamic consistency test
Amirhossein
Saali
amir.saali@srbiau.ac.ir
1
Department of Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Mohammad
Shokouhi
shokouhim@ripi.ir
2
Research Institute of Petroleum Industry (RIPI), Gas Research Division, P.O. Box 14665-137, Tehran, Iran
AUTHOR
Hossein
Sakhaeinia
h.sakhaeinia@iauctb.ac.ir
3
Department of Chemical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
[1] Sakhaeinia H, Taghikhani V, Jalili AH, Mehdizadeh A, Safekordi AA. Solubility of H2S in 1-(2-hydroxyethyl)-3-methylimidazolium ionic liquids with different anions. Fluid Phase Equilibria. 2010 Nov 25;298(2):303-9.
1
[2] Tagiuri A, Sumon KZ, Henni A. Solubility of carbon dioxide in three [Tf2N] ionic liquids. Fluid Phase Equilibria. 2014 Oct 25;380:39-47.
2
[3] Safavi M, Ghotbi C, Taghikhani V, Jalili AH, Mehdizadeh A. Study of the solubility of CO2, H2S and their mixture in the ionic liquid 1-octyl-3-methylimidazolium hexafluorophosphate: experimental and modelling. The Journal of Chemical Thermodynamics. 2013 Oct 1;65:220-32.
3
[4] Jalili AH, Safavi M, Ghotbi C, Mehdizadeh A, Hosseini-Jenab M, Taghikhani V. Solubility of CO2, H2S, and their mixture in the ionic liquid 1-octyl-3-methylimidazolium bis (trifluoromethyl) sulfonylimide. The Journal of Physical Chemistry B. 2012 Feb 28;116(9):2758-74.
4
[5] Yazdizadeh M, Rahmani F, Forghani AA. Thermodynamic modeling of CO2 solubility in ionic liquid ([Cn -mim][Tf2N]; n= 2, 4, 6, 8) with using Wong-Sandler mixing rule, Peng-Rabinson equation of state (EOS) and differential evolution (DE) method. Korean Journal of Chemical Engineering. 2011 Jan 1;28(1):246-51.
5
[6] Valderrama JO, Reátegui A, Sanga WW. Thermodynamic consistency test of vapor− liquid equilibrium data for mixtures containing ionic liquids. Industrial & Engineering Chemistry Research. 2008 Oct 4;47(21):8416-22.
6
[7] Valderrama JO, Robles PA. Thermodynamic consistency of high pressure ternary mixtures containing a compressed gas and solid solutes of different complexity. Fluid phase equilibria. 2006 Apr 5;242(1):93-102.
7
[8] Trejos VM, López JA, Cardona CA. Thermodynamic consistency of experimental VLE data for asymmetric binary mixtures at high pressures. Fluid Phase Equilibria. 2010 Jun 15;293(1):1-0.
8
[9] Faúndez CA, Millaldeo MF, Valderrama JO. The Kwak–Mansoori approach to the Peng–Robinson equation for determining the thermodynamic consistency of VLE in ethanol+ congener mixtures. Comptes Rendus Chimie. 2015 Aug 1;18(8):867-74.
9
[10] Valderrama JO, Faúndez CA, Campusano R. An overview of a thermodynamic consistency test of phase equilibrium data. Application of the versatile VPT equation of state to check data of mixtures containing a gas solute and an ionic liquid solvent. The Journal of Chemical Thermodynamics. 2019 Apr 1;131:122-32.
10
[11] Faúndez CA, Díaz-Valdés JF, Valderrama JO. Consistency test of solubility data of ammonia in ionic liquids using the modified Peng–Robinson equation of Kwak and Mansoori. Fluid Phase Equilibria. 2013 Jun 25;348:33-8.
11
[12] Eslamimanesh A, Mohammadi AH, Salamat Y, Shojaei MJ, Eskandari S, Richon D. Phase behavior of mixture of supercritical CO2 + ionic liquid: Thermodynamic consistency test of experimental data. AIChE Journal. 2013 Oct 1;59(10):3892-913.
12
[13] Valderrama JO, Robles PA. Critical properties, normal boiling temperatures, and acentric factors of fifty ionic liquids. Industrial & Engineering Chemistry Research. 2007 Feb 14;46(4):1338-44.
13
[14] Valderrama JO, Sanga WW, Lazzús JA. Critical properties, normal boiling temperature, and acentric factor of another 200 ionic liquids. Industrial & Engineering Chemistry Research. 2008 Feb 20;47(4):1318-30.
14
[15] Yokozeki A, Shiflett MB. Vapor–liquid equilibria of ammonia + ionic liquid mixtures. Applied Energy. 2007 Dec 1;84(12):1258-73.
15
[16] Yokozeki A. Solubility of refrigerants in various lubricants. International Journal of Thermophysics. 2001 Jul 1;22(4):1057-71.
16
[17] Van Ness HC, and Abbott MM. Classical Thermodynamics of Nonelectrolyte Solutions. New York: McGraw-Hill; 1982.
17
[18] Smith JM, Van Ness HC, and Abbott MM. Introduction to Chemical Engineering Thermodynamics, 6th ed. New York: McGraw-Hill; 2003.
18
[19] Valderrama JO, Alvarez VH. A versatile thermodynamic consistency test for incomplete phase equilibrium data of high-pressure gas–liquid mixtures. Fluid Phase Equilibria. 2004 Dec 10;226:149-59.
19
[20] Valderrama JO, Zavaleta J. Thermodynamic consistency test for high pressure gas–solid solubility data of binary mixtures using genetic algorithms. The Journal of Supercritical fluids. 2006 Nov 1;39(1):20-9.
20
[21] Valderrama JO, Reátegui A, Sanga WW. Thermodynamic consistency test of vapor-liquid equilibrium data for mixtures containing ionic liquids. Industrial & Engineering Chemistry Research. 2008 Oct 4;47(21):8416-22.
21
[22] Zoubeik M, Mohamedali M, Henni A. Experimental solubility and thermodynamic modeling of CO2 in four new imidazolium and pyridinium-based ionic liquids. Fluid Phase Equilibria. 2016 Jul 15;419:67-74.
22
[23] Tagiuri A, Sumon KZ, Henni A. Solubility of carbon dioxide in three [Tf2N] ionic liquids. Fluid Phase Equilibria. 2014 Oct 25;380:39-47.
23
[24] Zoubeik M, Henni A. Experimental and thermodynamic study of CO2 solubility in promising [TF2N and DCN] ionic liquids. Fluid Phase Equilibria. 2014 Aug 25;376:22-30.
24
ORIGINAL_ARTICLE
Geomechanical Sanding Prediction in Oil Fields by Wellbore Stability Charts
Sand production is a universally encountered issue during the exploration of unconsolidated sandstone reservoirs particularly during production. The production of sand particles with the reservoir fluids depends on the stress around a wellbore and the properties of the reservoir rocks and fluids. Therefore, it is crucial to predict under what production conditions sanding will occur and when sand control is needed to come up with the optimal field development plan. This paper presents new geomechanical stability charts for Oman that have been generated to predict sand production in sandstone formations during the production process. The produced stability charts simplified the complicated task of geomechanical analysis, and they are ready for direct applications by petroleum engineers with no need to be specialized in rock mechanics. This was achieved by utilizing a three-dimensional model which was previously justified. The applied model utilized the linear poroelastic constitutive model for the stresses around a borehole in conjunction with Mogi-Coulomb law to predict the failure of sandstone formations. In this work, moreover, the optimum well trajectories for Omani oil fields are reported.
https://jchpe.ut.ac.ir/article_72630_65771336c7937022172e48be8cebce6e.pdf
2019-12-01
237
244
10.22059/jchpe.2019.278184.1271
Critical drawdown pressure
Mogi-Coulomb criterion
optimum well path
sand production
wellbore stability
Nadia
Al Khalifin
naadia111@yahoo.com
1
Department of Petroleum and Chemical Engineering, Sultan Qaboos University, Muscat, Oman
AUTHOR
Adel
Al-Ajmi
ajmi@squ.edu.om
2
Department of Petroleum and Chemical Engineering, Sultan Qaboos University, Muscat, Oman
LEAD_AUTHOR
Hamoud
Al-Hadrami
hadrami@squ.edu.om
3
Department of Petroleum and Chemical Engineering, Sultan Qaboos University, Muscat, Oman
AUTHOR
[1] Aborisade O. Practical Approach to Effective Sand Prediction, Control and Management. [Doctoral dissertation]. Abuja: African University of Science and technology; 2010.
1
[2] Bianco LC, Halleck PM. Mechanisms of arch instability and sand production in two-phase saturated poorly consolidated sandstones. InSPE European Formation Damage Conference 2001 Jan 1. Society of Petroleum Engineers.
2
[3] Osisanya SO. Practical guidelines for predicting sand production. InNigeria Annual International Conference and Exhibition 2010 Jan 1. Society of Petroleum Engineers.
3
[4] Zare-Reisabadi MR, Kaffash A, Shadizadeh SR. Determination of optimal well trajectory during drilling and production based on borehole stability. International Journal of Rock Mechanics and Mining Sciences. 2012 Dec 1;56:77-87.
4
[5] Al-Ajmi A, Zimmerman R. A new 3D stability model for the design of non-vertical wellbores. InGolden Rocks 2006, The 41st US Symposium on Rock Mechanics (USRMS) 2006 Jan 1. American Rock Mechanics Association.
5
[6] Ewy RT. Wellbore-stability predictions by use of a modified Lade criterion. SPE Drilling & Completion. 1999 Jun 1;14(02):85-91.
6
[7] Al-Ajmi A. Wellbore stability analysis based on a new true-triaxial failure criterion, [Doctoral dissertation]. Stockholm: KTH; 2006.
7
[8] Chabook M, Al-Ajmi A, Isaev V. The role of rock strength criteria in wellbore stability and trajectory optimization. International Journal of Rock Mechanics and Mining Sciences. 2015;100(80):373-8.
8
[9] Elyasi A, Goshtasbi K. Using different rock failure criteria in wellbore stability analysis. Geomechanics for Energy and the Environment. 2015 Jul 1;2:15-21.
9
[10] Rahimi R. The effect of using different rock failure criteria in wellbore stability analysis. [Master Thesis]. Missouri: Missouri University of Science and Technology; 2014.
10
[11] Al-Shaaibi S. Three Dimentional Modeling For Predicting Sand Production. [Master Thesis]. Oman: Sultan Qaboos University; 2011.
11
[12] McLean MR, Addis MA. Wellbore stability: the effect of strength criteria on mud weight recommendations. InSPE annual technical conference and exhibition 1990 Jan 1. Society of Petroleum Engineers.
12
[13] Ramos GG, Mouton DE, Wilton BS. Integrating rock mechanics with drilling strategies in a tectonic belt, offshore Bali, Indonesia. InSPE/ISRM Rock Mechanics in Petroleum Engineering 1998 Jan 1. Society of Petroleum Engineers.
13
[14] Kingsborough RH, Williams AF, Hillis RR. Borehole instability on the Northwest Shelf of Australia. InSPE Asia-Pacific Conference 1991 Jan 1. Society of Petroleum Engineers.
14
[15] Onaisi A, Locane J, Razimbaud A. Stress related wellbore instability problems in deep wells in ABK field. InAbu Dhabi International Petroleum Exhibition and Conference 2000 Jan 1. Society of Petroleum Engineers.
15
[16] Awal MR, Khan MS, Mohiuddin MA, Abdulraheem A, Azeemuddin M. A new approach to borehole trajectory optimisation for increased hole stability. InSPE middle east oil show 2001 Jan 1. Society of Petroleum Engineers.
16
[17] Yi X. Numerical and analytical modeling of sanding onset prediction. [Doctoral dissertation]. College Station, Texas: Texas A&M University; 2004.
17
[18] Yi X, Valko PP, Russell JE. Predicting critical drawdown for the onset of sand production. InSPE International Symposium and Exhibition on Formation Damage Control 2004 Jan 1. Society of Petroleum Engineers.
18
[19] Ewy RT, Ray P, Bovberg CA, Norman PD, Goodman HE. Openhole stability and sanding predictions by 3D extrapolation from hole collapse tests. InSPE Annual Technical Conference and Exhibition 1999 Jan 1. Society of Petroleum Engineers.
19
[20] Al-Ajmi AM, Al-Harthy MH. Probabilistic wellbore collapse analysis. Journal of Petroleum Science and Engineering. 2010 Nov 1;74(3-4):171-7.
20
[21] Al-Aamri M. Geomechanicist, Petroleum Development of Oman. Personal communication, 2017.
21
[22] Al-Awad MN, Al-Ahaidib TY. Estimating the amount of free sand in the yielded zone around vertical and horizontal oil wells. InSPE Technical Symposium of Saudi Arabia Section 2005 Jan 1. Society of Petroleum Engineers.
22
[23] Araujo Guerrero EF, Alzate GA, Arbelaez-Londono A, Pena S, Cardona A, Naranjo A. Analytical prediction model of sand production integrating geomechanics for open hole and cased–perforated wells. InSPE Heavy and Extra Heavy Oil Conference: Latin America 2014 Sep 24. Society of Petroleum Engineers.
23
[24] Fjar E, Holt RM, Raaen AM, Risnes R, Horsrud P. Petroleum related rock mechanics. Elsevier; 2008 Jan 4.
24
[25] Kanfar MF, Chen Z, Rahman SS. Risk-controlled wellbore stability analysis in anisotropic formations. Journal of Petroleum Science and Engineering. 2015 Oct 1;134:214-22.
25
[26] Małkowski P. Behaviour of joints in sandstones during the shear test. Acta Geodyn. Geomater. 2015;12:399-410.
26
[27] Volonte G, Scarfato F, Brignoli M. Sand prediction: a practical finite-element 3D approach for real field applications. InSPE Annual Technical Conference and Exhibition 2010 Jan 1. Society of Petroleum Engineers.
27
[28] Wang H, Sharma MM. A fully 3-D, multi-phase, poro-elasto-plastic model for sand production. InSPE Annual Technical Conference and Exhibition 2016 Sep 26. Society of Petroleum Engineers.
28
[29] Al Khalifin NS. Geomechanical Stability Charts For Sanding Prediction [Master Thesis]. Oman: Sultan Qaboos University; 2017.
29
[30] Shaheed B. Wellbore stability Charts for Shale Formations During Drilling Operations [Master Thesis]. Oman: Sultan Qaboos University; 2017.
30
[31] Al-Ajmi AM, Zimmerman RW. A new well path optimization model for increased mechanical borehole stability. Journal of Petroleum Science and Engineering. 2009 Nov 1;69(1-2):53-62.
31
ORIGINAL_ARTICLE
New Insight on Deformation of Walnut/Ceramic Proppant Pack under Closure Stress in Hydraulic Fracture: Numerical Investigation
This study is an attempt to investigate the mechanical behavior of proppant packs deforming under compression loading. A generalized confined compression test (CCT) was simulated in the present study to investigate the deformation of walnut/ceramic proppants against compression. In this way, the CCT was simulated using ABAQUS explicit code. Unlike ordinary CCT, we obtained permeability of compressed packs through image processing of deformed packs. It was observed that a pack with small particles could markedly withstand deformation, however, at the expense of having lower permeability. Also, selecting a proper proppant pack strongly depends on the prevailing stress regime, where at low stress (<30 MPa) uniform walnut pack has the same permeability as a medley of walnut/ceramic pack. But, at greater stresses (> 40 Mpa), the pack with more ceramic is the best choice. Mixtures of walnut and ceramic proppants showed greatly strength improvement compared to similar cases with pure walnut granules. As a result, making use of such packing is highly recommended due to significant mechanical stability and also being of lower price compared to packs of pure ceramic granules.
https://jchpe.ut.ac.ir/article_72351_5d4d81c1e32fb5cf59e749a74ec5c199.pdf
2019-12-01
245
251
10.22059/jchpe.2019.278547.1274
Confined compression test
Deformation
Hydraulic fracture
Permeability
Proppant
Mohammad Hasan
Badizad
badizad@gmail.com
1
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
Amir Hossein
Saeedi Dehaghani
asaeedi@modares.ac.ir
2
Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
[1] Liang F, Sayed M, Al-Muntasheri GA, Chang FF, Li L. A comprehensive review on proppant technologies. Petroleum. 2016 Mar 1;2(1):26-39.
1
[2] Yao Y, Wang W, Keer LM. An energy based analytical method to predict the influence of natural fractures on hydraulic fracture propagation. Engineering Fracture Mechanics. 2018 Feb 15;189:232-45.
2
[3] Patel PS, Robart CJ, Ruegamer M, Yang A. Analysis of US hydraulic fracturing fluid system and proppant trends. InSPE Hydraulic Fracturing Technology Conference 2014 Feb 4. Society of Petroleum Engineers
3
[4] Tomac I, Gutierrez M. Micromechanics of proppant agglomeration during settling in hydraulic fractures. Journal of Petroleum Exploration and Production Technology. 2015 Dec 1;5(4):417-34.
4
[5] El-M. Shokir EM, Al-Quraishi AA. Experimental and numerical investigation of proppant placement in hydraulic fractures. Petroleum Science and Technology. 2009 Oct 21;27(15):1690-703.
5
[6] Peng HH, Lin CK, Chung YC. Effects of Particle Stiffness on Mechanical Response of Granular Solid Under Confined Compression. Procedia Engineering. 2014 Jan 1;79:143-52.
6
[7] Munjiza A, Owen DR, Bicanic N. A combined finite-discrete element method in transient dynamics of fracturing solids. Engineering computations. 1995 Feb 1;12(2):145-74.
7
[8] Guo J, Luo B, Lu C, Lai J, Ren J. Numerical investigation of hydraulic fracture propagation in a layered reservoir using the cohesive zone method. Engineering Fracture Mechanics. 2017 Dec 1;186:195-207.
8
[9] Kulkarni MC, Ochoa OO. Creating novel granular mixtures as proppants: Insights to shape, size, and material considerations. Mechanics of Advanced Materials and Structures. 2017 May 19;24(7):605-14.
9
[10] Xia L, Yvonnet J, Ghabezloo S. Phase field modeling of hydraulic fracturing with interfacial damage in highly heterogeneous fluid-saturated porous media. Engineering Fracture Mechanics. 2017 Dec 1;186:158-80.
10
[11] Carrier B, Granet S. Numerical modeling of hydraulic fracture problem in permeable medium using cohesive zone model. Engineering Fracture Mechanics. 2012 Jan 1;79:312-28.
11
[12] Kulkarni MC, Ochoa OO. Mechanics of light weight proppants: A discrete approach. Composites Science and Technology. 2012 May 2;72(8):879-85.
12
[13] Kulkarni MC, Ochoa OO. Light weight composite proppants: Computational and experimental study. Mechanics of Advanced Materials and Structures. 2012 Jan 1;19(1-3):109-18.
13
[14] ankowiak T, Lodygowski T. Identification of parameters of concrete damage plasticity constitutive model. Foundations of Civil and Environmental Engineering. 2005 Jun;6(1):53-69.
14
[15] Xu P, Yu B. Developing a new form of permeability and Kozeny–Carman constant for homogeneous porous media by means of fractal geometry. Advances in Water Resources. 2008 Jan 1;31(1):74-81.
15
ORIGINAL_ARTICLE
Prediction of methanol loss by hydrocarbon gas phase in hydrate inhibition unit by back propagation neural networks
Gas hydrate often occurs in natural gas pipelines and process equipment at high pressure and low temperature. Methanol as a hydrate inhibitor injects to the potential hydrate systems and then recovers from the gas phase and re-injects to the system. Since methanol loss imposes an extra cost on the gas processing plants, designing a process for its reduction is necessary. In this study, an accurate back propagation neural network (BPNN) is designed for the prediction of methanol loss by the gas phase as a function of temperature, pressure, and methanol composition in the aqueous phase. Different configurations of BPNN were trained, tested, and a configuration providing the smallest absolute average relative deviation (AARD%) was chosen as an optimum structure. Finally, comparisons made among the accuracy of the developed BPNN model, process simulators, and probabilistic neural network (PNN). Results confirm that the designed BPNN model is more accurate than the other considered predictive tools. The BPNN provided an AARD=5.75% for prediction of experimental data, while Aspen-HYSYS, Aspen-Plus, and PNN presented an AARD% of 9.71, 12.57, and 13.27, respectively.
https://jchpe.ut.ac.ir/article_72626_e8c5f414089dfc31c97753b638fef111.pdf
2019-12-01
253
264
10.22059/jchpe.2019.283971.1288
Artificial Neural Networks
commonly used process simulators
hydrocarbon gas phase
hydrate inhibition unit
methanol loss
Behzad
Vaferi
vaferi@iaushiraz.ac.ir
1
Young Researchers and Elite Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran
LEAD_AUTHOR
[1] Ghaedi H, Javanmardi J, Rasoolzadeh A, Mohammadi AH. Experimental Study and Thermodynamic Modeling of Methane Hydrate Dissociation Conditions in the Simultaneous Presence of BMIM-BF4 and Ethanol in Aqueous Solution. Journal of Chemical & Engineering Data. 2018 Apr 16;63(5):1724-32.
1
[2] Hammerschmidt EG. Formation of gas hydrates in natural gas transmission lines. Industrial & Engineering Chemistry. 1934 Aug 1;26(8):851-5.
2
[3] Kvamme B, Selvåg J, Saeidi N, Kuznetsova T. Methanol as a hydrate inhibitor and hydrate activator. Physical Chemistry Chemical Physics. 2018;20(34):21968-87.
3
[4] Hammerschmidt EG. Gas hydrate formations, A further study on their prevention and elimination from natural gas pipe lines. Gas. 1939 May;15(5):30-4.
4
[5] Covington, Kimberly C., John T. Collie III, and Steven D. Behrens. "Selection of hydrate suppression methods for gas streams." 78th GPA Annual Convention, Nashville, TN. 1999.
5
[6] Esteban A, Hernandez V, Lunsford K. Exploit the benefits of methanol. InProceedings of the 79th Gas Processors Association Annual Convention (GPA’00) 2000 Mar.
6
[7] Eslamimanesh A, Mohammadi AH, Richon D, Naidoo P, Ramjugernath D. Application of gas hydrate formation in separation processes: A review of experimental studies. The Journal of Chemical Thermodynamics. 2012 Mar 1;46:62-71.
7
[8] Iraci LT, Essin AM, Golden DM. Solubility of methanol in low-temperature aqueous sulfuric acid and implications for atmospheric particle composition. The Journal of Physical Chemistry A. 2002 Apr 25;106(16):4054-60.
8
[9] Bahadori A, Vuthaluru HB. Prediction of methanol loss in vapor phase during gas hydrate inhibition using Arrhenius-type functions. Journal of loss Prevention in the Process Industries. 2010 May 1;23(3):379-84.
9
[10] Bahadori A, Vuthaluru HB. Predictive tool for the estimation of methanol loss in condensate phase during gas hydrate inhibition. Energy & Fuels. 2010 Apr 14;24(5):2999-3002.
10
[11] Ghiasi MM, Arabloo M, Bahadori A, Zendehboudi S. Prediction of methanol loss in liquid hydrocarbon phase during natural gas hydrate inhibition using rigorous models. Journal of Loss Prevention in the Process Industries. 2015 Jan 1;33:1-9.
11
[12] Yousefinejad S, Eftekhari R, Honarasa F, Zamanian Z, Sedaghati F. Comparison between the gas-liquid solubility of methanol and ethanol in different organic phases using structural properties of solvents. Journal of Molecular Liquids. 2017 Sep 1;241:861-9.
12
[13] Teixeira AM, de Oliveira Arinelli L, de Medeiros JL, Ofélia de Queiroz FA. Recovery of thermodynamic hydrate inhibitors methanol, ethanol and MEG with supersonic separators in offshore natural gas processing. Journal of Natural Gas Science and Engineering. 2018 Apr 1;52:166-86.
13
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ORIGINAL_ARTICLE
Sliding Mode Control For Heartbeat Electrocardiogram Tracking Problem
In this paper, we have exploited the first-order sliding mode control method to track the ECG data of the human heart by three different nonlinear control laws. In order to lessen the intrinsic chattering of the classic sliding mode control system, smooth function approximations of the control input, by means of the hyperbolic tangent and the saturation function, were used. The fast Fourier transform was used to evaluate the average chattering frequency of the control inputs. The synthesized control schemes namely SMC-sign, SMC-tanh, and SMC-sat, were able to track the real-world ECG signal with an average root mean square error of 0.0306 and a chattering frequency of 92.7 Hz. The findings show that the sliding mode controllers can be implemented in electronic artificial pacemakers to provide the intended results successfully. Based on today's electronics, the involved frequency range (556.4 Hz for the worst case) is quite acceptable and practical.
https://jchpe.ut.ac.ir/article_72827_8f3dbedfe92c179ea0e2782589b6eb47.pdf
2019-12-01
265
272
10.22059/jchpe.2019.286911.1292
chattering phenomenon
electrocardiogram signal
electronic pacemaker
human heart
nonlinear control
Sliding mode control
Hooman
Fatoorehchi
hfatoorehchi@ut.ac.ir
1
School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Sohrab Ali
Ghorbanian
ghorban@ut.ac.ir
2
School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
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