Volume 4, Issue 2, December 2020, Page: 21-30
Prediction of Asphaltene Precipitation During Gas Injection Using Hybrid Genetic Algorithm and Particle Swarm Optimisation
Ikyerga Emmanuel, Department of Petroleum Engineering, University of Ibadan, Ibadan, Nigeria
Alawode Adeolu, Department of Petroleum Engineering, University of Ibadan, Ibadan, Nigeria
Falode Olugbenga Adebanjo, Department of Petroleum Engineering, University of Ibadan, Ibadan, Nigeria
Received: Mar. 17, 2020;       Accepted: Apr. 8, 2020;       Published: Oct. 26, 2020
DOI: 10.11648/j.ajaic.20200402.12      View  59      Downloads  7
Abstract
Asphaltenes are precipitated and deposited during gas injection and this causes pore throat reduction, permeability reduction and wettability reversal. The result is reduced oil produced thereby leading to sizable revenue loss by field operators. To mitigate or completely prevent the occurrence of this phenomenon, this work has utilised Hybrid Genetic Algorithm Particle Swarm Optimisation-Artificial Neural Network (HGAPSO-ANN) for predicting the amount of asphaltenes deposited in the reservoir during gas injection. A number of methods are available for predicting the amount of asphaltenes deposited but some of them are either too expensive to execute or fraught with errors and deviations. This is due to the nature of asphaltene which is complicated and ambiguous. Some of the methods in existence include correlation with solvent properties, thermodynamic models and recently connectionist models (neural networks). There is however, no publication in the literature on using hybrid algorithms with neural networks to predict asphaltene precipitation during gas injection and this becomes an interesting area of research considering the enormous benefits that would be obtained from a robust hybrid asphaltene precipitation prediction model. The developed model performed well with an AARE of 0.09. This is lower than AARE values reported by Hue et al (2000), Rostami and Manshad (2010), Manshad et al (2015) which were 0.183, 0.153, and 0.121 respectively From the results of the model it can be seen that HGAPSO-ANN is more accurate in predicting asphaltene precipitation than other existing predictive models consulted. This method can therefore, be used as a decision making tool by field operators to set up procedures for the prevention or mitigation of asphaltene precipitation during gas injection. This will help prevent revenue losses and increase profitability of recovering hydrocarbons using gas injection.
Keywords
Asphaltene Precipitation, Artificial Neural Network, Gentic Algorithm, Particle Swarm Optimization, Porous Media
To cite this article
Ikyerga Emmanuel, Alawode Adeolu, Falode Olugbenga Adebanjo, Prediction of Asphaltene Precipitation During Gas Injection Using Hybrid Genetic Algorithm and Particle Swarm Optimisation, American Journal of Applied and Industrial Chemistry. Vol. 4, No. 2, 2020, pp. 21-30. doi: 10.11648/j.ajaic.20200402.12
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Chung Frank, Partha Sarathi, & Ray Jones (1991). “Modeling of Asphaltene and Wax Precipitation”. Topical Report, National Institute for Petroleum and Energy Research.
[2]
Hasanvand M. Z, Mohammad Ali Ahmadi, & Reza Mosayebi Behbahani (2015). “Solving Asphaltene Precipitation Issue in Vertical Wells Via Redesigning of Production Facilities”. Petroleum I.
[3]
Hammami, A., Phelps, C. H., Monger-McClure, T. Little T. M.. 2000. Asphaltene Precipitation from Live Oils: An Experimental Investigation of Onset Conditions and Reversibility. Energy Fuels 14 (1): 14-18. http://dx.doi.org/10.1021/ef990104z.
[4]
Joshi, N. B., Mullins, O. C., Jamaluddin, A. et al. 2001. Asphaltene Precipitation from Live Crude Oil. Energy Fuels 15 (4): 979-986. http://dx.doi.org/10.1021/ef010047l.
[5]
Eigner Manfred, “Asphaltenes” OIlfieldwiki. (July) 2017 .
[6]
Nasri Zarrin & Bahram Dabir (2009). “Effects of Asphaltene Deposition on Oil Reservoir Characteristic Including Two-phase Flow” Journal of the Japan Petroleum Institute 52 (1) (online).
[7]
Minssieux, L. 1997. Core Damage From Crude Asphaltene Deposition. Presented at the International Symposium on Oilfield Chemistry, Houston, 18-21 February. SPE-37250-MS. http://dx.doi.org/10.2118/37250-MS.
[8]
Rassamdana, H., Dabir, B., Nematy, M. et al. 1996. Asphalt flocculation and deposition: I. The onset of precipitation. AIChE J. 42 (1): 10-22. http://dx.doi.org/10.1002/aic.690420104.
[9]
Hirschberg, A., deJong, L. N. J., Schipper, B. A. et al. 1984. Influence of Temperature and Pressure on Asphaltene Flocculation. SPE Journal. 24 (3): 283-293. SPE-11202- 0 PA. http://dx.doi.org/10.2118/11202-PA.
[10]
Abedini Ali, Siavash Ashoorib, Farshid Torabi, “Reversibility of asphaltene precipitation in porous and non-porous media”, Elsevier. (June) (2011) 129-134.
[11]
Leontaritis, K. J., Amaefule, J. O., and Charles, R. E. 1994. A Systematic Approach for the Prevention and Treatment of Formation Damage Caused by Asphaltene Deposition. SPE Prod & Oper 9 (3): 157–164. SPE-23810-PA. http://dx.doi.org/10.2118/23810-PA.
[12]
Takhar S., P. D. Ravenscroft, D. C. A. Nicoll. “Prediction of Asphaltene Deposition During Production. Model description and Experimental Details”. Society of petroleum Engineers, Paper prepared for presentation at the European Formation Damage Conference held in The Hague, The Netherlands, 15-16 May 1995.
[13]
Moradi1 S., M. Dabiri, B. Dabir, D. Rashtchian and M. A. Emadi. “Investigation of asphaltene precipitation in miscible gas injection Processes: experimental study and Modeling”. Brazilian Journal of Chemical Engineering. Vol. 29, No. 03, pp. 665 - 676, July - September, 2012 www.abeq.org.br/bjche.
[14]
Wang, J. X., Brower, K. R., and Buckley, J. S. 2000. Observation of Asphaltene Destabilization at elevated Temperature and Pressure. SPE Journal. 5 (4): 420-425. SPE-67856-PA. http://dx.doi.org/10.2118/67856-PA.
[15]
Wu Jianzhong, John M. Prausnitz, and Abbas Firoozabadi. “Molecular-Thermodynamic Framework for Asphaltene-Oil Equilibria”. AIChE Journal. May 1998 Vol. 44, No. 5: 1188-1199.
[16]
De Boer RB et al (1995) Screening of crude oils for asphalt precipitation: theory, practice, and the selection of inhibitors. SPE Prod Facil 10 (1): 55–61.
[17]
Arya Alay, Xiaodong Liang, Nicolas von Solms, and Georgios M. Kontogeorgis “Prediction of Gas Injection Effect on Asphaltene Precipitation Onset using the Cubic and Cubic-Plus Association Equations of State”. Energy & Fuels. 15 Feb 2017 (web) DOI: 10.1021/acs.energyfuels.6b03328.
[18]
Lashkari Hossein, Riyaz Kharrat & Ali Reza Khaz'ali. “Prediction of asphaltene precipitation during gas injection”. Petroleum Science and Technology, 35: 3, 271-278. 22 Mar 2017 http://dx.doi.org/10.1080/10916466.2016.1244548.
[19]
Hornick, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366.
[20]
Hornik, K., Stinchcombe, M., White, H., 1990. Universal approximation of an unknown mapping and itsderivatives using multilayer feed forward networks. Neural Networks 3 (5), 551–600.
[21]
Zendehboudi Sohrab, Ali Shafiei, Alireza Bahadori, Lesley A. James, Ali Elkamel, Ali Lohi. “Asphaltene precipitation and deposition in oil reservoirs –Technical aspects, experimental and hybrid neural networkpredictive tools”. Chemical engineering research and design (2014). 9 2 857–875.
[22]
Ahmadi Mohammad Ali & Seyed Reza Shadizadeh. “New Approach for Prediction of Asphaltene Precipitation Due To Natural Depletion by Using Evolutionary Algorithm Concept”. Elsevier Ltd. (2012). http://dx.doi.org/10.1016/j.fuel.2012.05.050.
[23]
Hamra Enas Ahmed Abu (2016). “Combine Genetic Algorithm and Particle Swarm Optimization Approach for Neural Network Classification” A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master in Information Technology. The Islamic University Gaza.
[24]
Manshad A. K., Habib Rostani, Hojjat Rezael, Seyed Moein Hosseini. “Application of Artificial Neural Network-Particle Swarm Optimization Algorithm for Prediction of Asphaltene Precipitation During Gas Injection Process and Comparison with Gaussian Process Algorithm”. Journal of Energy Resources Technology. Vol. 137 Nov. 2015.
[25]
Dorsey, R. E., & Mayer W. J., “Genetic Algorithms for Estimation Problems with Multiple Optima, Non-Differentiability, and other Irregular Features,” Journal of Business and Economic Statistics, 1995, 13 (1), 53-66.
[26]
Wikipedia, “Asphaltene”. (12 Aug 2017) https://en.wikipedia.org/wiki/Asphaltene.
[27]
Assareh E., M. A. Behrang, M. R. Assari, A. Ghanbarzadeh. “Application of PSO (particle swarm optimization) and GA (genetic algorithm) Techniques on Demand Estimation of oil in Iran”. Energy 35 (2010) 5223-5229.
[28]
Poli, R. (2008). "Analysis of the publications on the applications of particle swarm optimisation" (PDF). Journal of Artificial Evolution and Applications. 2008: 1-10. doi: 10.1155/2008/685175.
[29]
Juang Chia-Feng. “A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design”. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 34, no. 2, April 2004.
[30]
Hu, Y.-F., Chen, G.-J., Yang, J.-T., and Guo, T.-M., 2000, “A Study on the Application of Scaling Equation for Asphaltene Precipitation,” Fluid Phase Equilib., 171 (1), pp. 181–195.
[31]
Rostami, H., and Manshad, A. K., 2010, “Prediction of Asphaltene Precipitation in Live and Tank Crude Oil Using Gaussian Process Regression,” Pet. Sci. Technol., 31 (9), pp. 913–922.
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