Skip to main content
Log in

Efficient screening of enhanced oil recovery methods and predictive economic analysis

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

Abstract

Oil demand for economic development around the world is rapidly increasing. Moreover, oil production rates are getting a peak in mature reservoirs and tending to decline in the near future, which has led to considerable researches on enhanced oil recovery (EOR) methods. Therefore, an efficient technical and economical screening to appropriate selection of EOR methods can make savings in time and cost. The purpose of this communication is to present a method to select an efficient EOR process and investigate its economic parameters. A database of reservoir parameters of rock and fluid properties along with successful EOR techniques has been collected and analyzed. First, an artificial neural network (ANN) was developed to classify the EOR methods technically. Then, an economical EOR screening model was designed, and then, future cash flows on the use of EOR methods were predicted. The results show that the ANN system can select proper EOR methods and classify them. Moreover, the obtained results indicate that the economic analysis performed in this study is efficient and useful to predict future cash flows.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. \( {\text{MSE}} = \frac{{\sum_{i = 1}^{n} {({\text{EOR}}_{{{\text{pred}}_{i} }} } - {\text{EOR}}_{{\exp_{i} }} )^{2} }}{n}. \)

References

  1. Abbas E, Song ChL (2011) Artificial intelligence selection with capability of editing a new parameter for EOR screening criteria. Journal of Engineering Science and Technology 6:628–638

    Google Scholar 

  2. Abdulrazzagh YZ, Jebri KK, El-Honi M (2000) Economic evaluation of enhanced oil recovery. In: SPE international oil and gas conference and exhibition. Beijing, China

  3. Aladasani A, Bai B (2010) Recent developments and updated screening criteria of enhanced oil recovery techniques. In: Proceeding of the CPS/SPE international oil and gas conference and exhibition. Beijing, China

  4. Al Adsani A, Bai B (2011) Analysis of EOR projects and update screening criteria. Journal of Petroleum Science and Engineering 79:10–24

    Article  Google Scholar 

  5. Alvardo V, Ranson A, Hernandez K, Manrique E, Matheus J, Liscano T, Prosperi N (2002) Selection of EOR/IOR opportunities based on machine learning. In: Proceeding of the 13th European petroleum conference. Aberdeen, UK

  6. Barzegari D, Ayatollahi S, Zerafat MM, Roosta AA (2010) EOR screening using artificial intelligence bayesian network. In: Proceeding of 14th international oil, gas and petrochemical congress. Tehran, Iran

  7. Dickson JL, Leahy-Dios A, Wylie PL (2010) Development of improved hydrocarbon recovery screening methodologies. In: Proceeding of SPE improved oil recovery symposium held in Tulsa. Oklahoma, USA

  8. Hajizadeh Y (2007) Intelligent prediction of reservoir fluid viscosity. In: Proceeding of SPE production and operations symposium held in Oklahoma City. Oklahoma, USA

  9. Haykin S (1999) Neural networks, a comprehensive foundation. Macmillan College Publishing Co., New York

    MATH  Google Scholar 

  10. Holmstrom L, Koistinen P (1992) Using additive noise in back-propagation training. IEEE Transaction on Neural Networks 3:24–38

    Google Scholar 

  11. Gharbi RBC (2000) An expert system for selecting and designing EOR processes. Journal of Petroleum Science and Engineering 27:33–47

    Google Scholar 

  12. Gharbi R (2005) Application of an expert system to optimize reservoir performance. Journal of Petroleum Science and Engineering 49:261–273

    Google Scholar 

  13. Guerillot DR (1988) EOR screening with an expert system. In: The symposium on petroleum industry applications of microcomputers. San Jose, CA

  14. Ibatullin RR, Ibragimov NG, Khisamov RS, Podymov ED, Shutov AA (2002) Application and method based on artificial intelligence for selection of structures and screening of technologies for enhanced oil recovery. In: Proceeding of SPE/DOE improved oil recovery symposium. Tulsa, Oklahoma, USA

  15. Lee JY, Shin HJ, Lim JS (2011) Selection and evaluation of enhanced oil recovery method using artificial neural network. Geosystem Engineering 14:157–164

    Google Scholar 

  16. Liu Y, Starzyk JA, Zhu Z (2008) Optimized approximation algorithm in neural networks without overfitting. IEEE Transaction on Neural Networks 19(6):983–995

    Google Scholar 

  17. MATLAB User’s Guide (2002) Version 4, neural network toolbox. The MathWorks Inc., Natick, MA

    Google Scholar 

  18. Mc Coy ST, Rubin ES (2009) The effect of high oil prices on EOR project economics. Energy Procedia 1:4143–4150

    Google Scholar 

  19. (1998) Worldwide EOR survey. Oil Gas J 96(16):59–74. http://www.ogj.com/articles/print/volume-96/issue-16/in-this-issue/general-interest/1998-worldwide-eor-survey.html

  20. (2006) Worldwide EOR survey. Oil Gas J 104(15):45–57. http://www.ogj.com/articles/print/volume-104/issue-15/special-report/2006-worldwide-eor-survey.html

  21. (2008) Worldwide EOR survey. Oil Gas J 106(15):47–59. http://www.ogj.com/articles/print/volume-106/issue-15/drilling-production/special-report-2008-worldwide-eor-survey.html

  22. Parada CH, Ertekin T (2012) A new screening tool for improved oil recovery methods using artificial neural network. In: Proceeding of SPE western regional meeting held in Bakersfield. California, USA

  23. Parkinson WJ, Luger GF, Bretz RE, Osowski J (1994) Using an expert system to explore enhanced oil recovery methods. Computers & Electrical Engineering 20:181–197

    Google Scholar 

  24. Piotrowski AP, Napiorkowski JJ (2013) A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling. Journal of Hydrology 476:97–111

    Google Scholar 

  25. Poggio T, Girosi F (1990) Networks for approximation and learning. Proceedings of the IEEE 78(9):1481–1497

    Google Scholar 

  26. Shokir EM, Goda HM, Sayyouh MH, Fattah KA (2002) Selection and evaluation EOR method using artificial intelligence. In: Proceeding of the 26rd annual international technical conference and exhibition. Abuja, Nigeria

  27. Taber JJ, Martin FD (1983) Technical screening guides for the enhanced recovery of oil. In: Proceeding of the 58th annual technical conference and exhibition. San Francisco, California, USA

  28. Taber JJ, Martin FD, Seright RS (1997) EOR screening criteria revisited—part 1: introduction to screening criteria and enhanced recovery field projects. In: Proceeding of the SPE/DOE improved oil recovery symposium. Tulsa, Oklahoma, USA

  29. Taber JJ, Martin FD, Seright RS (1997) EOR screening criteria revisited—part 2: application and impact of oil prices. In: Proceeding of the SPE/DOE improved oil recovery symposium. Tulsa, Oklahoma, USA

  30. Zerafat MM, Ayatollahi S, Mehranbod N, Barzegari D (2011) Bayesian network analysis as a tool for efficient EOR screening. In: Proceeding of the SPE enhanced oil recovery conference. Kuala Lumpur, Malaysia

Download references

Acknowledgments

The authors are grateful to IOR Research Institute; NIOC R&T for their support.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Mohammad Nikookar or Amir H. Mohammadi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kamari, A., Nikookar, M., Sahranavard, L. et al. Efficient screening of enhanced oil recovery methods and predictive economic analysis. Neural Comput & Applic 25, 815–824 (2014). https://doi.org/10.1007/s00521-014-1553-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1553-9

Keywords

Navigation