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Published in: Arabian Journal for Science and Engineering 2/2020

09-12-2019 | Research Article - Petroleum Engineering

Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques

Authors: Chahrazed Benamara, Kheira Gharbi, Menad Nait Amar, Boudjema Hamada

Published in: Arabian Journal for Science and Engineering | Issue 2/2020

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Abstract

The paraffin particles can promote and be involved in the formation of deposits which can lead to plugging of oil production facilities. In this work, an experimental prediction of wax appearance temperature (WAT) has been performed on 59 Algerian crude oil samples using a pour point tester. In addition, a modeling investigation was done to create reliable WAT paradigms. To do so, gene expression programming and multilayers perceptron optimized with Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian regularization algorithm were implemented. To generate these models, some parameters, namely density, viscosity, pour point, freezing point and wax content in crude oils, have been used as input parameters. The results reveal that the developed models provide satisfactory results. Furthermore, the comparison between these models in terms of accuracy indicates that MLP-LMA has the best performances with an overall average absolute relative error of 0.23% and a correlation coefficient of 0.9475.

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Literature
1.
go back to reference Elsharkawy, A.M.; Al-Sahhaf, T.A.; Fahim, M.A.: Wax deposition from Middle East crudes. Fuel 79, 1047–1055 (2000)CrossRef Elsharkawy, A.M.; Al-Sahhaf, T.A.; Fahim, M.A.: Wax deposition from Middle East crudes. Fuel 79, 1047–1055 (2000)CrossRef
2.
go back to reference Sarica, C.; Panacharoensawad, E.: Review of paraffin deposition research under multiphase flow conditions. Energy Fuels 26, 3968–3978 (2012)CrossRef Sarica, C.; Panacharoensawad, E.: Review of paraffin deposition research under multiphase flow conditions. Energy Fuels 26, 3968–3978 (2012)CrossRef
3.
go back to reference Kelechukwu, E.M.; Al-Salim, H.S.; Saadi, A.: Prediction of wax deposition problems of hydrocarbon production system. J. Pet. Sci. Eng. 108, 128–136 (2013)CrossRef Kelechukwu, E.M.; Al-Salim, H.S.; Saadi, A.: Prediction of wax deposition problems of hydrocarbon production system. J. Pet. Sci. Eng. 108, 128–136 (2013)CrossRef
4.
go back to reference Chien-Hou, W.; Kang-Shi, W.; Shuler, P.J.; Tang, Y.: others: measurement of wax deposition in paraffin solutions. Am. Inst. Chem. Eng. AIChE J. 48, 2107 (2002)CrossRef Chien-Hou, W.; Kang-Shi, W.; Shuler, P.J.; Tang, Y.: others: measurement of wax deposition in paraffin solutions. Am. Inst. Chem. Eng. AIChE J. 48, 2107 (2002)CrossRef
5.
go back to reference Kelland, M.A.: Production Chemicals for the Oil and Gas Industry. CRC Press, Boca Raton (2014)CrossRef Kelland, M.A.: Production Chemicals for the Oil and Gas Industry. CRC Press, Boca Raton (2014)CrossRef
6.
go back to reference Robustillo, M.D.; Coto, B.; Martos, C.; Espada, J.J.: Assessment of different methods to determine the total wax content of crude oils. Energy Fuels 26, 6352–6357 (2012)CrossRef Robustillo, M.D.; Coto, B.; Martos, C.; Espada, J.J.: Assessment of different methods to determine the total wax content of crude oils. Energy Fuels 26, 6352–6357 (2012)CrossRef
7.
go back to reference Kamari, A.; Mohammadi, A.H.; Bahadori, A.; Zendehboudi, S.: A reliable model for estimating the wax deposition rate during crude oil production and processing. Pet. Sci. Technol. 32, 2837–2844 (2014)CrossRef Kamari, A.; Mohammadi, A.H.; Bahadori, A.; Zendehboudi, S.: A reliable model for estimating the wax deposition rate during crude oil production and processing. Pet. Sci. Technol. 32, 2837–2844 (2014)CrossRef
8.
go back to reference Bidmus, H.O.; Mehrotra, A.K.: Solids deposition during “cold flow” of wax- solvent mixtures in a flow-loop apparatus with heat transfer. Energy Fuels 23, 3184–3194 (2009)CrossRef Bidmus, H.O.; Mehrotra, A.K.: Solids deposition during “cold flow” of wax- solvent mixtures in a flow-loop apparatus with heat transfer. Energy Fuels 23, 3184–3194 (2009)CrossRef
9.
go back to reference Aiyejina, A.; Chakrabarti, D.P.; Pilgrim, A.; Sastry, M.K.S.: Wax formation in oil pipelines: a critical review. Int. J. Multiph. Flow 37, 671–694 (2011)CrossRef Aiyejina, A.; Chakrabarti, D.P.; Pilgrim, A.; Sastry, M.K.S.: Wax formation in oil pipelines: a critical review. Int. J. Multiph. Flow 37, 671–694 (2011)CrossRef
10.
go back to reference Al-Yaari, M.; et al.: Paraffin wax deposition: mitigation and removal techniques. In: SPE Saudi Arabia Section Young Professionals Technical Symposium (2011) Al-Yaari, M.; et al.: Paraffin wax deposition: mitigation and removal techniques. In: SPE Saudi Arabia Section Young Professionals Technical Symposium (2011)
11.
go back to reference Taheri-Shakib, J.; Shekarifard, A.; Naderi, H.: Characterization of the wax precipitation in Iranian crude oil based on Wax Appearance Temperature (WAT): part 1. The influence of electromagnetic waves. J. Pet. Sci. Eng. 161, 530–540 (2018)CrossRef Taheri-Shakib, J.; Shekarifard, A.; Naderi, H.: Characterization of the wax precipitation in Iranian crude oil based on Wax Appearance Temperature (WAT): part 1. The influence of electromagnetic waves. J. Pet. Sci. Eng. 161, 530–540 (2018)CrossRef
12.
go back to reference Huang, Z.; Zheng, S.; Fogler, H.S.: Wax Deposition: Experimental Characterizations, Theoretical Modeling, and Field Practices. CRC Press, Boca Raton (2016)CrossRef Huang, Z.; Zheng, S.; Fogler, H.S.: Wax Deposition: Experimental Characterizations, Theoretical Modeling, and Field Practices. CRC Press, Boca Raton (2016)CrossRef
13.
go back to reference Javadian, H.; Asadollahpour, S.; Ruiz, M.; Sastre, A.M.; Ghasemi, M.; Asl, S.M.H.; Masomi, M.: Using fuzzy inference system to predict Pb(II) removal from aqueous solutions by magnetic Fe3O4/H2SO4-activated Myrtus Communis leaves carbon nanocomposite. J. Taiwan Inst. Chem. Eng. 91, 186–199 (2018)CrossRef Javadian, H.; Asadollahpour, S.; Ruiz, M.; Sastre, A.M.; Ghasemi, M.; Asl, S.M.H.; Masomi, M.: Using fuzzy inference system to predict Pb(II) removal from aqueous solutions by magnetic Fe3O4/H2SO4-activated Myrtus Communis leaves carbon nanocomposite. J. Taiwan Inst. Chem. Eng. 91, 186–199 (2018)CrossRef
14.
go back to reference Ayegba, P.O.; Abdulkadir, M.; Hernandez-Perez, V.; Lowndes, I.S.; Azzopardi, B.J.: Applications of artificial neural network (ANN) method for performance prediction of the effect of a vertical 90° bend on an air–silicone oil flow. J. Taiwan Inst. Chem. Eng. 74, 59–64 (2017)CrossRef Ayegba, P.O.; Abdulkadir, M.; Hernandez-Perez, V.; Lowndes, I.S.; Azzopardi, B.J.: Applications of artificial neural network (ANN) method for performance prediction of the effect of a vertical 90° bend on an air–silicone oil flow. J. Taiwan Inst. Chem. Eng. 74, 59–64 (2017)CrossRef
15.
go back to reference Raja, M.A.Z.; Ahmed, T.; Shah, S.M.: Intelligent computing strategy to analyze the dynamics of convective heat transfer in MHD slip flow over stretching surface involving carbon nanotubes. J. Taiwan Inst. Chem. Eng. 80, 935–953 (2017)CrossRef Raja, M.A.Z.; Ahmed, T.; Shah, S.M.: Intelligent computing strategy to analyze the dynamics of convective heat transfer in MHD slip flow over stretching surface involving carbon nanotubes. J. Taiwan Inst. Chem. Eng. 80, 935–953 (2017)CrossRef
16.
go back to reference Ahmadi, M.-A.; Ahmadi, M.H.; Alavi, M.F.; Nazemzadegan, M.R.; Ghasempour, R.; Shamshirband, S.: Determination of thermal conductivity ratio of CuO/ethylene glycol nanofluid by connectionist approach. J. Taiwan Inst. Chem. Eng. 91, 383–395 (2018)CrossRef Ahmadi, M.-A.; Ahmadi, M.H.; Alavi, M.F.; Nazemzadegan, M.R.; Ghasempour, R.; Shamshirband, S.: Determination of thermal conductivity ratio of CuO/ethylene glycol nanofluid by connectionist approach. J. Taiwan Inst. Chem. Eng. 91, 383–395 (2018)CrossRef
17.
go back to reference Menad, N.A.; Noureddine, Z.; Hemmati-Sarapardeh, A.; Shamshirband, S.; Mosavi, A.; Chau, K.: Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming. Eng. Appl. Comput. Fluid Mech. 13, 724–743 (2019) Menad, N.A.; Noureddine, Z.; Hemmati-Sarapardeh, A.; Shamshirband, S.; Mosavi, A.; Chau, K.: Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming. Eng. Appl. Comput. Fluid Mech. 13, 724–743 (2019)
18.
go back to reference Menad, N.A.; Noureddine, Z.; Hemmati-Sarapardeh, A.; Shamshirband, S.: Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: application to thermal enhanced oil recovery processes. Fuel 242, 649–663 (2019)CrossRef Menad, N.A.; Noureddine, Z.; Hemmati-Sarapardeh, A.; Shamshirband, S.: Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: application to thermal enhanced oil recovery processes. Fuel 242, 649–663 (2019)CrossRef
19.
go back to reference Menad, N.A.; Noureddine, Z.: An efficient methodology for multi-objective optimization of water alternating CO2 EOR process. J. Taiwan Inst. Chem. Eng. 99, 154–165 (2019)CrossRef Menad, N.A.; Noureddine, Z.: An efficient methodology for multi-objective optimization of water alternating CO2 EOR process. J. Taiwan Inst. Chem. Eng. 99, 154–165 (2019)CrossRef
20.
go back to reference Menad, N.A.; Hemmati-Sarapardeh, A.; Varamesh, A.; Shamshirband, S.: Predicting solubility of CO2 in brine by advanced machine learning systems: application to carbon capture and sequestration. J. CO2 Util. 33, 83–95 (2019)CrossRef Menad, N.A.; Hemmati-Sarapardeh, A.; Varamesh, A.; Shamshirband, S.: Predicting solubility of CO2 in brine by advanced machine learning systems: application to carbon capture and sequestration. J. CO2 Util. 33, 83–95 (2019)CrossRef
21.
go back to reference Amirian, E.; Fedutenko, E.; Yang, C.; Chen, Z.; Nghiem, L.: Artificial neural network modeling and forecasting of oil reservoir performance. In: Moshirpour, M., Far, B., Alhajj, R. (eds.) Applications of Data Management and Analysis. Lecture Notes in Social Networks, pp. 43–67. Springer, Cham (2018) Amirian, E.; Fedutenko, E.; Yang, C.; Chen, Z.; Nghiem, L.: Artificial neural network modeling and forecasting of oil reservoir performance. In: Moshirpour, M., Far, B., Alhajj, R. (eds.) Applications of Data Management and Analysis. Lecture Notes in Social Networks, pp. 43–67. Springer, Cham (2018)
22.
go back to reference Amirian, E.; Leung, J.Y.; Zanon, S.; Dzurman, P.: Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs. Expert Syst. Appl. 42, 723–740 (2015)CrossRef Amirian, E.; Leung, J.Y.; Zanon, S.; Dzurman, P.: Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs. Expert Syst. Appl. 42, 723–740 (2015)CrossRef
23.
go back to reference Amirian, E.; Dejam, M.; Chen, Z.: Performance forecasting for polymer flooding in heavy oil reservoirs. Fuel 216, 83–100 (2018)CrossRef Amirian, E.; Dejam, M.; Chen, Z.: Performance forecasting for polymer flooding in heavy oil reservoirs. Fuel 216, 83–100 (2018)CrossRef
24.
go back to reference Ahmadi, M.A.; Zendehboudi, S.; James, L.A.: Developing a robust proxy model of CO2 injection: coupling Box–Behnken design and a connectionist method. Fuel 215, 904–914 (2018)CrossRef Ahmadi, M.A.; Zendehboudi, S.; James, L.A.: Developing a robust proxy model of CO2 injection: coupling Box–Behnken design and a connectionist method. Fuel 215, 904–914 (2018)CrossRef
25.
go back to reference Ahmadi, M.A.; Ebadi, M.; Shokrollahi, A.; Majidi, S.M.J.: Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl. Soft Comput. 13, 1085–1098 (2013)CrossRef Ahmadi, M.A.; Ebadi, M.; Shokrollahi, A.; Majidi, S.M.J.: Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl. Soft Comput. 13, 1085–1098 (2013)CrossRef
26.
go back to reference Hemmati-Sarapardeh, A.; Varamesh, A.; Husein, M.M.; Karan, K.: On the evaluation of the viscosity of nanofluid systems: modeling and data assessment. Renew. Sustain. Energy Rev. 81, 313–329 (2018)CrossRef Hemmati-Sarapardeh, A.; Varamesh, A.; Husein, M.M.; Karan, K.: On the evaluation of the viscosity of nanofluid systems: modeling and data assessment. Renew. Sustain. Energy Rev. 81, 313–329 (2018)CrossRef
27.
go back to reference Ameli, F.; Hemmati-Sarapardeh, A.; Schaffie, M.; Husein, M.M.; Shamshirband, S.: Modeling interfacial tension in N 2/n-alkane systems using corresponding state theory: application to gas injection processes. Fuel 222, 779–791 (2018)CrossRef Ameli, F.; Hemmati-Sarapardeh, A.; Schaffie, M.; Husein, M.M.; Shamshirband, S.: Modeling interfacial tension in N 2/n-alkane systems using corresponding state theory: application to gas injection processes. Fuel 222, 779–791 (2018)CrossRef
28.
go back to reference Ameli, F.; Hemmati-Sarapardeh, A.; Dabir, B.; Mohammadi, A.H.: Determination of asphaltene precipitation conditions during natural depletion of oil reservoirs: a robust compositional approach. Fluid Phase Equilib. 412, 235–248 (2016)CrossRef Ameli, F.; Hemmati-Sarapardeh, A.; Dabir, B.; Mohammadi, A.H.: Determination of asphaltene precipitation conditions during natural depletion of oil reservoirs: a robust compositional approach. Fluid Phase Equilib. 412, 235–248 (2016)CrossRef
29.
go back to reference Hemmati-Sarapardeh, A.; Ameli, F.; Dabir, B.; Ahmadi, M.; Mohammadi, A.H.: On the evaluation of asphaltene precipitation titration data: modeling and data assessment. Fluid Phase Equilib. 415, 88–100 (2016)CrossRef Hemmati-Sarapardeh, A.; Ameli, F.; Dabir, B.; Ahmadi, M.; Mohammadi, A.H.: On the evaluation of asphaltene precipitation titration data: modeling and data assessment. Fluid Phase Equilib. 415, 88–100 (2016)CrossRef
32.
go back to reference Kamari, A.; Rahimzadeh, A.; Mohammadi, A.H.; Ramjugernath, D.: Evaluation of wax disappearance temperatures in hydrocarbon fluids using soft computing approaches. Pet. Sci. Technol. 37, 829–836 (2019)CrossRef Kamari, A.; Rahimzadeh, A.; Mohammadi, A.H.; Ramjugernath, D.: Evaluation of wax disappearance temperatures in hydrocarbon fluids using soft computing approaches. Pet. Sci. Technol. 37, 829–836 (2019)CrossRef
33.
go back to reference Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson, Upper Saddle River (2001) Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson, Upper Saddle River (2001)
34.
go back to reference Lashkarbolooki, M.; Hezave, A.Z.; Ayatollahi, S.: Artificial neural network as an applicable tool to predict the binary heat capacity of mixtures containing ionic liquids. Fluid Phase Equilib. 324, 102–107 (2012)CrossRef Lashkarbolooki, M.; Hezave, A.Z.; Ayatollahi, S.: Artificial neural network as an applicable tool to predict the binary heat capacity of mixtures containing ionic liquids. Fluid Phase Equilib. 324, 102–107 (2012)CrossRef
35.
go back to reference Rostami, A.; Hemmati-Sarapardeh, A.; Shamshirband, S.: Rigorous prognostication of natural gas viscosity: smart modeling and comparative study. Fuel 222, 766–778 (2018)CrossRef Rostami, A.; Hemmati-Sarapardeh, A.; Shamshirband, S.: Rigorous prognostication of natural gas viscosity: smart modeling and comparative study. Fuel 222, 766–778 (2018)CrossRef
36.
go back to reference Fletcher, R.: Practical Methods of Optimization. Wiley, New York (2013)MATH Fletcher, R.: Practical Methods of Optimization. Wiley, New York (2013)MATH
37.
go back to reference Toth, E.; Brath, A.; Montanari, A.: Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol. 239, 132–147 (2000)CrossRef Toth, E.; Brath, A.; Montanari, A.: Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol. 239, 132–147 (2000)CrossRef
38.
go back to reference Yue, Z.; Songzheng, Z.; Tianshi, L.: Bayesian regularization BP neural network model for predicting oil-gas drilling cost. In: 2011 International Conference on Business Management and Electronic Information (BMEI), pp. 483–487 (2011) Yue, Z.; Songzheng, Z.; Tianshi, L.: Bayesian regularization BP neural network model for predicting oil-gas drilling cost. In: 2011 International Conference on Business Management and Electronic Information (BMEI), pp. 483–487 (2011)
39.
go back to reference Ferreira, C.: Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst. 13, 87–129 (2001)MATH Ferreira, C.: Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst. 13, 87–129 (2001)MATH
40.
go back to reference Teodorescu, L.; Sherwood, D.: High energy physics event selection with gene expression programming. Comput. Phys. Commun. 178, 409–419 (2008)CrossRef Teodorescu, L.; Sherwood, D.: High energy physics event selection with gene expression programming. Comput. Phys. Commun. 178, 409–419 (2008)CrossRef
41.
go back to reference Hajirezaie, S.; Hemmati-Sarapardeh, A.; Mohammadi, A.H.; Pournik, M.; Kamari, A.: A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids. J. Nat. Gas Sci. Eng. 26, 1452–1459 (2015)CrossRef Hajirezaie, S.; Hemmati-Sarapardeh, A.; Mohammadi, A.H.; Pournik, M.; Kamari, A.: A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids. J. Nat. Gas Sci. Eng. 26, 1452–1459 (2015)CrossRef
42.
go back to reference Chen, G.; Fu, K.; Liang, Z.; Sema, T.; Li, C.; Tontiwachwuthikul, P.; Idem, R.: The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel 126, 202–212 (2014)CrossRef Chen, G.; Fu, K.; Liang, Z.; Sema, T.; Li, C.; Tontiwachwuthikul, P.; Idem, R.: The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel 126, 202–212 (2014)CrossRef
43.
go back to reference Shateri, M.; Ghorbani, S.; Hemmati-Sarapardeh, A.; Mohammadi, A.H.: Application of Wilcoxon generalized radial basis function network for prediction of natural gas compressibility factor. J. Taiwan Inst. Chem. Eng. 50, 131–141 (2015)CrossRef Shateri, M.; Ghorbani, S.; Hemmati-Sarapardeh, A.; Mohammadi, A.H.: Application of Wilcoxon generalized radial basis function network for prediction of natural gas compressibility factor. J. Taiwan Inst. Chem. Eng. 50, 131–141 (2015)CrossRef
Metadata
Title
Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques
Authors
Chahrazed Benamara
Kheira Gharbi
Menad Nait Amar
Boudjema Hamada
Publication date
09-12-2019
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 2/2020
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-019-04290-y

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