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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 289239, 10 pages
http://dx.doi.org/10.1155/2014/289239
Research Article

Oil Well Characterization and Artificial Gas Lift Optimization Using Neural Networks Combined with Genetic Algorithm

1Department of Electrical and Electronic Engineering, University of Ibadan, Nigeria
2School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, South Africa
3Department of Petroleum Engineering, University of Ibadan, Nigeria

Received 24 January 2014; Revised 23 March 2014; Accepted 8 April 2014; Published 22 May 2014

Academic Editor: Weihua Liu

Copyright © 2014 Chukwuka G. Monyei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Codas and E. Camponogara, “Mixed-integer linear optimization for optimal lift-gas allocation with well-separator routing,” European Journal of Operational Research, vol. 217, no. 1, pp. 222–231, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  2. T. Ray and R. Sarker, “Genetic algorithm for solving a gas lift optimization problem,” Journal of Petroleum Science and Engineering, vol. 59, no. 1-2, pp. 84–96, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. E. Camponogara and P. H. R. Nakashima, “Solving a gas-lift optimization problem by dynamic programming,” European Journal of Operational Research, vol. 174, no. 2, pp. 1220–1246, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. J. N. M. de Souza, J. L. de Medeiros, A. L. H. Costa, and G. C. Nunes, “Modeling, simulation and optimization of continuous gas lift systems for deepwater offshore petroleum production,” Journal of Petroleum Science and Engineering, vol. 72, no. 3-4, pp. 277–289, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Mahmudi and M. T. Sadeghi, “The optimization of continuous gas lift process using an integrated compositional model,” Journal of Petroleum Science and Engineering, vol. 108, pp. 321–327, 2013. View at Publisher · View at Google Scholar
  6. R. Sharma, K. Fjalestad, and B. Glemmestad, “Optimization of lift gas allocation in a gas lifted oil field as non-linear optimization problem,” Modeling, Identification and Control, vol. 33, no. 1, pp. 13–25, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. O. G. Santos, S. N. Bordalo, and F. J. S. Alhanati, “Study of the dynamics, optimization and selection of intermittent gas-lift methods—a comprehensive model,” Journal of Petroleum Science and Engineering, vol. 32, no. 2–4, pp. 231–248, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. E. P. Kanu, J. Mach, and K. E. Brown, “Economic approach to oil production and gas allocation in continuous gas lift,” Journal of Petroleum Technology, vol. 33, no. 10, pp. 1887–1892, 1981. View at Google Scholar · View at Scopus
  9. A. Ibitola and C. G. Monyei, “Genetic algorithm for students allocation to halls of residence using energy consumption as discriminant adaptive computing,” Information Systems, Development Informatics & Allied Research Journal, vol. 3, no. 4, pp. 31–36.
  10. C. G. Monyei and O. A. Fakolujo, “Optimized enhanced control system for the Unibadan’s virtual power plant project using genetic algorithm,” African Journal of Computing & ICT, vol. 6, no. 4, pp. 16–20, 2013. View at Google Scholar
  11. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, Mass, USA, 1989.
  12. A. O. Adewumi, B. A. Sawyerr, and M. M. Ali, “A heuristic solution to the university timetabling problem,” Engineering Computations, vol. 26, no. 8, pp. 972–984, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. A. R. Yildiz and F. Ozturk, “Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation,” Proceedings of the Institution of Mechanical Engineers B: Journal of Engineering Manufacture, vol. 220, no. 12, pp. 2041–2053, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. T. C. Nwaoha, Z. Yang, J. Wang, and S. Bonsall, “Application of genetic algorithm to risk-based maintenance operations of liquefied natural gas carrier systems,” Proceedings of the Institution of Mechanical Engineers E: Journal of Process Mechanical Engineering, vol. 225, no. 1, pp. 40–52, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Guo, X. Wang, and Y. Han, “The enhanced genetic algorithms for the optimization design,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI '10), pp. 2990–2994, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. A. O. Adewumi and M. M. Ali, “A multi-level genetic algorithm for a multi-stage space allocation problem,” Mathematical and Computer Modelling, vol. 51, no. 1-2, pp. 109–126, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  17. M. A. Arasomwan and A. O. Adewumi, “An adaptive velocity particle swarm optimization for high-dimensional function optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '13), pp. 2352–2359, 2013.
  18. S. Chetty and A. O. Adewumi, “Three new stochastic local search algorithms for continuous optimization problems,” Computational Optimization and Applications, vol. 56, no. 3, pp. 675–721, 2013. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet