Abstract
Precipitation and deposition of asphaltene have undesirable effects on the petroleum industry by increasing operational costs due to reduction of well productivity as well as catalyst poisoning. Herein we propose a reliable model for quantitative estimation of asphaltene precipitation. Scaling equation is the most powerful and popular model for accurate prediction of asphaltene precipitated out of solution in crudes without regard to complex nature of asphaltene. We employed a new mathematical-based approach known as alternating conditional expectation (ACE) technique for combining results of different scaling models in order to increase the accuracy of final estimation. Outputs of three well-known scaling equations, including Rassamdana (RE), Hu (HU), and Ashoori (AS), are input to ACE and the final output is produced through a nonlinear combination of scaling equations. The proposed methodology is capable of significantly increasing the precision of final estimation via a divide-and-conquer principle in which ACE functions as the combiner. Results indicate the superiority of the proposed method compared with other individual scaling equation models.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Gholami A, Asoodeh M, Bagheripour P. Fuzzy assessment of asphaltene stability in crude oils. J Dispersion Sci Technol, 2014, 35: 556–563
Gholami A, Asoodeh M, Bagheripour P. Smart determination of difference index for asphaltene stability evaluation. J Dispersion Sci Technol, 2014, 35: 572–576
Ghatee MH, Hemmateenejad B, Sedghamiz T, Khosousi T, Ayatollahi S, Seiedi O, Sayyad Amin J. Multivariate curve resolution alternating least-squares as a tool for analyzing crude oil extracted asphaltene samples. Energy Fuels, 2012, 26: 5663–5671
Kurup AS, Wang J, Subramani HJ, Buckley J, Creek JL, Chapman WJ. Revisiting asphaltene deposition tool (ADEPT): field application. Energy Fuels, 2012, 26: 5702–5710
Kord S, Miri R, Ayatollahi S, Escrochi M. Asphaltene deposition in carbonate rocks: experimental investigation and numerical simulation. Energy Fuels, 2012, 26: 6186–6199
Ahmadi MA, Golshadi M. Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. J Pet Sci Eng, 2012, 98–99: 40–49
Ahmadi MA, Shadizadeh SR. New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel, 2012, 102: 716–723
Tavakkoli M, Masihi M, Ghazanfari MH, Kharrat R. An improvement of thermodynamic micellization model for of asphaltene precipitation during gas injection in heavy crude prediction. Fluid Phase Equilib, 2011, 308: 153–163
Jafari Behbahani T, Ghotbi C, Taghikhani V, Shahrabadi A. Investigation on asphaltene deposition mechanisms during CO2 flooding processes in porous media: a novel experimental study and a modified model based on multilayer theory for asphaltene adsorption. Energy Fuels, 2012, 26: 5080–5091
Moradi S, Dabir B, Rashtchian D, Mahmoudi B. Effect of miscible nitrogen injection on instability, particle size distribution, and fractal structure of asphaltene aggregates. J Dispersion Sci Technol, 2012, 33: 763–770
Moradi S, Dabiri M, Dabir B, Rashtchian D, Emadi MA. Investigation of asphaltene precipitation in miscible gas injection processes: experimental study and modeling. Braz J Chem Eng, 2012, 29: 665–676
Salahshoor K, Zakeri S, Mahdavi S, Kharrat R. Khalifeh M. Asphaltene deposition prediction using adaptive neuro-fuzzy models based on laboratory measurements. Fluid Phase Equilib, 2013, 337: 89–99
Lawal KA, Crawshaw JP, Boek ES, Vesovik V. Experimental investigation of asphaltene deposition in capillary flow. Energy Fuels, 2012, 26: 2145–2153
Abu Tarboush BJ, Husein MM. Adsorption of asphaltenes from heavy oil onto in situ prepared NiO nanoparticles. J Colloid Interface Sci, 2012, 378: 64–69
Punnapala S, Vargas FM. Revisiting the PC-SAFT characterization procedure for an improved asphaltene precipitation prediction. Fuel, 2013, 108: 417–429
Shirani B, Nikazar M, Mousavi-Dehghani SA. Prediction of asphaltene phase behavior in live oil with CPA equation of state. Fuel, 2012, 97: 89–96
Asoodeh M, Gholami A, Bagheripour P. Renovating scaling equation through hybrid genetic algorithm-pattern search tool for asphaltene precipitation modeling. J Dispersion Sci Technol, 2014, 35: 607–611
Shirani B, Nikazar M, Naseri A, Mousavi-Dehghani SA. Modeling of asphaltene precipitation utilizing Association Equation of State. Fuel, 2012, 93: 59–66
Asoodeh M, Gholami A, Bagheripour P. Asphaltene precipitation of titration data modeling through committee machine with stochastically optimized fuzzy logic and optimized neural network. Fluid Phase Equilib, 2014, 364: 67–74
Nakhli H, Alizadeh A, Sadeghi Moghadam M, Afshari S, Kharrat R, Ghazanfari MH. Monitoring of asphaltene precipitation: experimental and modeling study. J Pet Sci Eng, 2011, 78: 384–395
Naimi SR, Gholami A, Asoodeh M. Prediction of crude oil asphaltene precipitation using support vector regression. J Dispersion Sci Technol, 2014, 35: 518–523
Panuganti SR, Vargas FM, Gonzalez DL, Kurup AS, Chapman WG. PC-SAFT characterization of crude oils and modeling of asphaltene phase behavior. Fuel, 2012, 93: 658–669
Wu J, Prausnitz JM, Firoozabadi A. Molecular-thermodynamic framework for asphaltene-oil equilibria. AIChE J, 1998, 44: 1188–1199
Mansoori GA. Modeling of asphaltene and other heavy organic depositions. J Pet Sci Eng, 1997, 17: 101–111
Rassamdana H, Dabir B, Nematy M, Farhani M, Sahimi M. Asphalt flocculation and deposition: I. the onset of precipitation. AIChE J, 1996, 42: 10–22
Rassamdana H, Farhani M, Dabir B, Mozaffarian M, Sahimi M. Asphalt flocculation and deposition. V. Phase behavior in miscible and immiscible injections. Energy Fuels, 1999, 13: 176–187
Hu YF, Guo TM. Effect of temperature and molecular weight of n-alkane precipitants on asphaltene precipitation. Fluid Phase Equilib, 2001, 192: 13–25
Ashoori S, Abedini A, Abedini R, Qorbani Nasheghi K. Comparison of scaling equation with neural network model for prediction of asphaltene precipitation. J Pet Sci Eng, 2010, 72: 186–194
Breiman L, Friedman JH. Estimating optimal transformations for multiple regression and correlation. J Am Stat Assoc, 1985, 80: 580–598
Hu YF, Chen GJ, Yang JT, Guo TM. A study on the application of scaling equation for asphaltene precipitation. Fluid Phase Equilib, 2000, 171: 181–195
Tang X, Zhou J. Nonlinear relationship between the real exchange rate and economic fundamentals: evidence from China and Korea. J Int Money Finance, 2013, 32: 304–323
Blanco M, Coello J, Maspoch S, Puigdomenech AR. Modelling of an environmental parameter by use of the alternating conditional expectation method. Chemom Intell Lab Syst, 1999, 46: 31–39
Xue G, Datta-Gupta A, Valko P, Blasingame T. Optimal transformations for multiple regression: application to permeability estimation from well logs. SPE Formation Evaluation, 1996: 115–130
Eissa M, Shokir EM. CO2 oil minimum miscibility pressure model for impure and pure CO2 streams. J Pet Sci Eng, 2007, 58: 173–185
Malallah A, Ghorbi R, Algharib M. Accurate estimation of the world crude oil PVT properties using graphical alternating conditional expectation. Energy Fuels, 2006, 20: 688–698
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gholami, A., Moradi, S., Asoodeh, M. et al. Asphaltene precipitation modeling through ACE reaping of scaling equations. Sci. China Chem. 57, 1774–1780 (2014). https://doi.org/10.1007/s11426-014-5253-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11426-014-5253-1