Skip to main content
SpringerLink
Log in

Application of evolutionary Gaussian processes regression by particle swarm optimization for prediction of dew point pressure in gas condensate reservoirs

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

One of the most critical quantities for characterizing a gas condensate reservoir is dew point pressure. But, accurate determination of dew point pressure is a very challengeable task in reservoir development. Experimental measurement of dew point pressure in PVT (Pressure, Volume, Temperature) cell is often difficult, especially in the case of lean retrograde gas condensate. So, different empirical correlations and equations of state are developed by researchers to calculate this property. Empirical correlations do not have ability to reliably duplicate the temperature behavior of constant composition fluids, and equations of state have convergence problem and need to be tuned against some experimental data. In addition, these approaches are not generalizable to unseen data, and they usually memorize the data used to develop them. In this paper, we develop an intelligent model to predict dew point pressure of gas condensate reservoirs using Gaussian processes optimized by particle swarm optimization. The developed model is generalizable and can estimate unseen data with the same distribution of training data accurately. Results show that the proposed method in this paper outperforms the previous published models and correlations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. A modified particle swarm optimizer (1998) doi:10.1109/ICEC.1998.699146

  2. Amiri S, Mehrnia MR, Barzegari D, Yazdani A (2011) An artificial neural network for prediction of gas holdup in bubble columns with oily solutions. Neural Comput Appl 20:487–494. doi:10.1007/s00521-011-0566-x

  3. Bertsimas D, Nohadani O (2010) Robust optimization with simulated annealing. J Glob Optim 48(2):323-334. doi:10.1007/s10898-009-9496-x

    Article  MATH  MathSciNet  Google Scholar 

  4. Carison M, Cawston W (1996) Obtaining pvt data for very sour retrograde gas and volatile oil reservoirs: a muti-disciplinary approach. SPE gas technology symposium, Calgary, Alberta, Canada, 28 Apr–1 May 1996

  5. Cessie SL, Houwelingen JCV (1992) Ridge estimators in logistic regression. Appl Stat 41(1):191–201. doi:10.2307/2347628

    Article  MATH  Google Scholar 

  6. Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of ECAL91—European conference on artificial life. Elsevier, Amsterdam

  7. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other Kernel-based learning methods. Cambridge University Press, Cambridge

    Book  Google Scholar 

  8. Danesh A (1998) PVT and phase behavior of petroleum reservoir fluids. Elsevier, Amsterdam

    Google Scholar 

  9. Darwiche PA (2009) Modeling and reasoning with Bayesian networks, 1st edn. Cambridge University Press, New York

    Book  Google Scholar 

  10. Dorigo M, Stützle T (2004) Ant colony optimization. Bradford Company, Scituate

    Book  MATH  Google Scholar 

  11. Elsharkawy A (2002) Predicting the dew point pressure for gas condensate reservoir: empirical models and equations of state. Fluid Phase Equilib 193:147–165

    Article  Google Scholar 

  12. Fausett L (ed) (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  13. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley, Boston

    Google Scholar 

  14. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10–18. doi:10.1145/1656274.1656278

    Article  Google Scholar 

  15. Hall MA (1999) Correlation-based feature selection for machine learning. Master’s thesis

  16. Han J, Kamber M (2006) Data mining:concepts and techniques. Morgan Kaufmann, Los Altos

    Google Scholar 

  17. Kennedy J, Eberhart R (2002) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, 1995, vol 4, pp 1942–1948. doi:10.1109/ICNN.1995.488968

  18. Khandelwal M (2011) Application of an expert system to predict thermal conductivity of rocks. Neural Comput Appl (2011). doi:10.1007/s00521-011-0573-y

  19. Marrufo I, Maita J, Him J, Rojas G (2001) Statistical forecast models to determine retrograde dew pressure and c +7 percentage of gas condensates on basis of production test date of eastern venezuelan reservoirs. SPE Latin American and Caribbean petroleum engineering conference, Buenos Aires, Argentina, 25–28 Mar 2001

  20. Mathworks: Matlab (2010) http://www.mathworks.com

  21. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) Yale: rapid prototyping for complex data mining tasks. In: Ungar L, Craven M, Gunopulos D, Eliassi-Rad T (eds) KDD ’06: proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 935–940

  22. Nemeth L, Kennedy H (1967) A correlation of dew point pressure with fluid composition and temperature. SPEJ 7(2):11477

    Google Scholar 

  23. Nowroozi S, Ranjbar M, Hashemipour H, Schaffie M (2009) Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs. Fuel Process Technol 90:452–457

    Article  Google Scholar 

  24. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57. doi:10.1007/s11721-007-0002-0

    Article  Google Scholar 

  25. Potsch K, Braeuer L (1996) A novel graphical method for determining dew point pressure of gas condensates. SPE European petroleum conference, Milan, Italy, 22–24 October 1996

  26. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data. ACM

  27. Rasmussen CE, Williams CK (2006) Gaussian process for machine learning. MIT Press, Cambridge

    Google Scholar 

  28. Ustn B, Melssen W, Buydens L (2006) Facilitating the application of support vector regression by using a universal pearson vii function based kernel. Chemom Intell Lab Syst 81:29–40

    Article  Google Scholar 

  29. Yin XC, Liu Q, Hao HW, Wang ZB, Huang K (2011) Fmi image based rock structure classification using classifier combination. Neural Comput Appl 20(7):955–963

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by a grant from research department of Persian Gulf University of Bushehr.

Author information

Authors and Affiliations

  1. Computer Engineering Department, School of Engineering, Persian Gulf University of Bushehr, 75168, Bushehr, Iran

    Habib Rostami

  2. Chemical Engineering Department, School of Engineering, Persian Gulf University of Bushehr, 75168, Bushehr, Iran

    Abbas Khaksar Manshad

Authors
  1. Habib Rostami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbas Khaksar Manshad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Habib Rostami.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rostami, H., Khaksar Manshad, A. Application of evolutionary Gaussian processes regression by particle swarm optimization for prediction of dew point pressure in gas condensate reservoirs. Neural Comput & Applic 24, 705–713 (2014). https://doi.org/10.1007/s00521-012-1275-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1275-9

Keywords

Search

Navigation