0
Research Papers: Petroleum Engineering

Application of Artificial Neural Network-Particle Swarm Optimization Algorithm for Prediction of Asphaltene Precipitation During Gas Injection Process and Comparison With Gaussian Process Algorithm

[+] Author and Article Information
Abbas Khaksar Manshad

Department of Petroleum Engineering,
Abadan Faculty of Petroleum Engineering,
Petroleum University of Technology,
Abadan, Iran

Habib Rostami

Department of Computer Engineering,
School of Engineering,
Persian Gulf University,
Bushehr 7516913817, Iran
e-mail: habib@pgu.ac.ir

Hojjat Rezaei, Seyed Moein Hosseini

Department of Petroleum Engineering,
Ahwaz Faculty of Petroleum Engineering,
Petroleum University of Technology (PUT),
Ahwaz, Iran

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received September 9, 2014; final manuscript received July 9, 2015; published online July 27, 2015. Assoc. Editor: Egidio Marotta.

J. Energy Resour. Technol 137(6), 062904 (Jul 27, 2015) (5 pages) Paper No: JERT-14-1291; doi: 10.1115/1.4031042 History: Received September 09, 2014

Asphaltene precipitation is a major problem in the oil production and transportation of oil. Changes in pressure, temperature, and composition of oil can lead to asphaltene precipitation. In the case of gas injection into oil reservoirs, the injected gas causes a change in oil composition and may lead to asphaltene precipitation. Accurate determination and prediction of the precipitated amount are vital, for this purpose there are several approaches such as experimental method, scaling equation, thermodynamics models, and neural network as the most recent ones. In this paper, we propose a new artificial neural network (ANN) optimized by particle swarm optimization (PSO) to predict the amount of asphaltene precipitation. This is conducted during the process of gas injection into oil reservoirs for enhanced oil recovery purposes. In the developed models, (1) oil composition, (2) temperature, (3) pressure, (4) oil specific gravity, (5) solvent mole percent, (6) solvent molecular weight, and (7) asphaltene content are considered as input parameters to the neural network. The weight of asphaltene and asphaltene content are considered as input parameters to the neural network and the weight of asphaltene precipitation as an output parameter. A comparison between the results of the proposed new model with Gaussian Process algorithm and previous research shows that the predictive model is more accurate.

FIGURES IN THIS ARTICLE
<>
Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Manshad, A. K. , Mofidi, A. M. , Shariatpanahi, F. , and Edalat, M. , 2008, “Developing of Scaling Equation With Function of Pressure to Determine Onset of Asphaltene Precipitation,” J. Jpn. Pet. Inst., 51(2), pp. 102–106. [CrossRef]
Manshad, A. K. , and Edalat, M. , 2008, “Application of Continuous Polydisperse Molecular Thermodynamics for Modeling Asphaltene Precipitation in Crude Oil Systems,” Energy Fuels, 22(4), pp. 2678–2686. [CrossRef]
Manshad, A. K. , Manshad, M. K. , and Ashoori, S. , 2012, “The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs,” Pet. Sci. Technol., 30(23), pp. 2450–2459. [CrossRef]
Speight, J. G. , Long, R. B. , and Trowbridge, T. D. , 1984, “Factors Influencing the Separation of Asphaltenes From Heavy Petroleum Feedstocks,” Fuel, 63(5), pp. 616–620. [CrossRef]
Ali, L. H. , and Al-Ghannam, K. A. , 1981, “Investigations Into Asphaltenes in Heavy Crude Oils. I. Effect of Temperature on Precipitation by Alkane Solvents,” Fuel, 60(11), pp. 1043–1046. [CrossRef]
Hirschberg, A. , Degong, L. N. J. , Schipper, B. A. , and Meijer, J. G. , 1984, “Influence of Temperature and Pressure on Asphaltene Flocculation,” Old SPE J., 24(3), pp. 283–293.
Srivastava, R. , Huang, S. S. , Dyer, S. B. , and Mourits, F. M. , 1994, “Heavy Oil Recovery by Subcritical Carbon Dioxide Flooding,” SPE Latin America/Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, Apr. 27–29.
Fadairo, A. , Anthony, A. , Churchill1, A. , and Olawale, D. , 2010, “Modeling of Wax Deposition During Oil Production Using a Two-Phase Flash Calculation,” Pet. Coal, 52(3), pp. 193–202.
Won, K. , 1986, “Thermodynamics for Solid Solution-Liquid-Vapor Equilibria: Wax Phase Formation From Heavy Hydrocarbon Mixtures,” Fluid Phase Equilib., 30(), pp. 265–279. [CrossRef]
Sivaraman, A. , Hu, Thomas, F. B. , Bennion, D. B. , and Jamaluddin, A. K. M. , Hycal Energy Research Laboratories Ltd. Calgary, Alberta, Canada.
Leontaritis, K. J. , and Mansoori, G. A. ,1988, “Asphaltene Deposition: A Survey of Field Experiences and Research Approaches,” J. Pet. Sci. Eng., 1(2), pp. 229–239. [CrossRef]
Fanchi, J. R. , 2007, PEH: Asphaltenes and Waxes, Society of Petroleum Engineers, Richardson, TX.
Sang, J. P. , and Mansoori, G. A. , 1988, “Aggregation and Deposition of Heavy Organics in Petroleum Crudes,” Energy Sources, 10(2), pp. 109–125. [CrossRef]
Kawanka, S. , Park, S. J. , and Mansoori, G. A. , 1991, “Organic Deposition From Reservoir Fluids: A Thermodynamic Predictive Technique,” SPE Res. Eng., 6(2), pp. 186–192.
MacMillan, D. J. , Tackett, J. E. , Jesee, M. A. , and Monger, T. G. A. , 1995, “Unified Approach to Asphaltene Precipitation: Laboratory Measurement and Modeling,” J. Pet. Technol., 47(9), pp. 788–793. [CrossRef]
Ghanei, M. , and Edalat, M. , 1996, “The Non-Regular Solubility Parameter Term for Predicting Onset and Amount of Asphaltene Precipitation,” Society of Petroleum Engineers, Richardson, TX, SPE Paper No. 67329.
Manshad, A. K. , 2004, “Investigation of Thermodynamic Modeling of Asphaltene Precipitation,” M.Sc. thesis, Amirkabir University of Technology, Tehran, Iran.
Mousavi, S. M. R. , Najafi, I. , Ghazanfari, M. H. , and Amani, M. , 2012, “Comparison of Ultrasonic Wave Radiation Effects on Asphaltene Aggregation in Toluene–Pentane Mixture Between Heavy and Extra Heavy Crude Oils,” ASME J. Energy Resour. Technol., 134(2), p. 022001. [CrossRef]
Mahmoud, M. , and Nasr-El-Din, H. , 2014, “Challenges During Shallow and Deep Carbonate Reservoirs Stimulation,” ASME J. Energy Resour. Technol, 137(1), p. 012902. [CrossRef]
Pan, H. , and Firoozabadi, A. , 2000, “Thermodynamic Micellization Model for Asphaltene Precipitation From Reservoir Crudes at High Pressures and Temperatures,” SPE Prod. Facil., 15(1), pp. 58–65. [CrossRef]
Victorov, A. , and Firoozabadi, A. , 1996, “Thermodynamics of Asphaltene Deposition Using a Micellization Model,” AIChE J., 42(6), pp. 1753–1764. [CrossRef]
Rassamdana, H. , Dabir, B. , Nematy, M. , Farhani, M. , and Sahimi, M. , 1996, “Asphalt Flocculation and Deposition: I. The Onset of Precipitate,” AIChE J., 42(1), pp. 10–21. [CrossRef]
Manshad, A. K. , Manshad, M. K. , Rostami, H. , Mojdeh Mohseni, S. , and Mohseni, S. M. , 2013, “Developing a Scaling Equation as a Function of Pressure and Temperature to Determine the Amount of Asphaltene Precipitation,” Pet. Sci. Technol., 31(23), pp. 2169–2177. [CrossRef]
Khandelwal, M. , 2011, “Application of an Expert System to Predict Thermal Conductivity of Rocks,” Neural Comput. Appl., 21(6), pp. 1341–1347. [CrossRef]
Yin, X. , Liu, Q. , Hao, H. , Wang, Z. , and Huang, K. , 2011, “GMI Image Based Rock Structure Classification Using Classifier Combination,” Neural Comput. Appl., 20(7), pp. 955–963. [CrossRef]
Rostami, H. , and Mansha, A. K. , 2014, “A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields,” ASME J. Energy Resour. Technol, 136(2), p. 024502. [CrossRef]
Samuel, R. , and Yao, D. , 2013, “DrillString Vibration With Hole-Enlarging Tools: Analysis and Avoidance,” ASME J. Energy Resour. Technol., 135(3), p. 032904. [CrossRef]
Ghasemloonia, A. , Geoff Rideout, D. , and Butt, S. D. , 2013, “Vibration Analysis of a Drillstring in Vibration-Assisted Rotary Drilling: Finite Element Modeling With Analytical Validation,” ASME J. Energy Resour. Technol., 135(3), p. 032902. [CrossRef]
Zahedi, G. , Fazlalib, A. R. , Hosseinia, S. M. , Pazukic, G. R. , and Sheikhattara, L. , 2009, “Prediction of Asphaltene Precipitation in Crude Oil,” J. Pet. Sci. Eng., 68(3), pp. 218–222. [CrossRef]
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. [CrossRef]
Manshad, A. K. , Manshad, M. K. , and Ashoori, S. , 2012, “The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs,” Pet. Sci. Technol., 30(23), pp. 2450–2459. [CrossRef]
Rajasekaran, S. , and Vijayalakshmi Pai, G. A. , 2004, Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications, Prentice-Hall, Delhi, India.
Gharbi, R. , 1997, “Estimating the Isothermal Compressibility Coefficient of Under Saturated Middle East Crudes Using Neural Networks,” Energy Fuels, 11(2), pp. 372–378. [CrossRef]
Hornik, K. , Stinchcombe, M. , and White, H. , 1989, “Multilayer Feedforward Networks Areuniversal Approximators,” Neural Networks, 2(5), pp. 359–366. [CrossRef]
Brown, M. , and Harris, C. J. , 1994, Neurofuzzy Adaptive Modelling and Control, Prentice Hall, Upper Saddle River, NJ.
Sayyad, H. , Manshad, A. K. , and Rostami, H. , 2014, “Application of Hybrid Neural Particle Swarm Optimization Algorithm for Prediction of MMP,” Fuel, 116, pp. 625–633. [CrossRef]
Eberhart, R. , and Kennedy, J. , 1995, “A New Optimizer Using Particle Swarm Theory,” Sixth International Symposium on Micro Machine and Human Science (MHS'95), Nagoya, Japan, Oct. 4–6, pp. 39–43.
Goldberg, D. E. , 1989, Genetic Algorithms in Search, Optimization, and Machine Learning , Addison-Wesley Longman, Boston.
Bertsimas, D. , and Nohadani, O. , 2010, “Robust Optimization With Simulated Annealing,” 48(2), J. Global Optim., pp. 323–334. [CrossRef]
Kennedy, J. , and Eberhart, R. , 1995, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks (ICNN'95), Perth, WA, Nov. 27–Dec. 1, Vol. 4, pp. 1942–1948.
Liu, D. , and Hou, Z.-G. , 2007, Advances in Neural Networks: 4th International Symposium on Neural Networks (ISNN 2007), Nanjing, China, June 3–7, Springer-Verlag, New York.
Eberhart, R. , Simpson, P. , and Dobbins, R. , 1996, Computational Intelligence PC Tools, Academic Press Professional, Inc., San Diego, CA.
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. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Structural design of three layer feed-forward ANNs

Grahic Jump Location
Fig. 2

ANN inputs for prediction of asphaltene precipitation

Grahic Jump Location
Fig. 3

Flow chart for ANN-PSO

Grahic Jump Location
Fig. 4

Network performance in data training

Grahic Jump Location
Fig. 5

Network performance in data validating

Grahic Jump Location
Fig. 6

Network performance in data testing

Grahic Jump Location
Fig. 7

Scatter plot of model prediction values versus measured value

Grahic Jump Location
Fig. 8

Comparison of model prediction values with measured value

Grahic Jump Location
Fig. 9

Impact of input variables on asphaltene precipitation amount

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In