Table of Contents
Journal of Petroleum Engineering
Volume 2015, Article ID 714541, 16 pages
http://dx.doi.org/10.1155/2015/714541
Research Article

Examination of Experimental Designs and Response Surface Methods for Uncertainty Analysis of Production Forecast: A Niger Delta Case Study

1Department of Petroleum Engineering, African University of Science and Technology (AUST), Km 10 Airport Road, Galadimawa, Abuja, Nigeria
2No. 1, Odi Street, Old GRA, Port Harcourt, Rivers State, Nigeria

Received 18 November 2014; Accepted 22 February 2015

Academic Editor: Mikhail Panfilov

Copyright © 2015 Akeem O. Arinkoola and David O. Ogbe. 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. D. E. Steagall and D. J. Schiozer, “Uncertainty analysis in reservoir production forecasts during appraisal and pilot production phases,” in Proceedings of the SPE Reservoir Simulation Symposium, SPE 66399, Houston, Tex, USA, February 2001. View at Publisher · View at Google Scholar
  2. N. Almeida, D. J. Schiozer, E. L. Ligero, and C. Maschio, “History matching using uncertainty analysis,” in Proceedings of the SPE Canadian International Petroleum Conference, SPE 153604, Calgary, Canada, June 2003.
  3. C. H. Peng and R. Gupta, “Experimental design in deterministic modelling: assessing significant uncertainties,” in Proceedings of the SPE Asia Pacific Oil and Gas Conference, SPE 80537, Jakarta, Indonesia, September 2003.
  4. C. Amudo, T. Graf, N. R. Haris, R. Dandecar, F. Ben Amor, and R. S. May, “Experimental design and response surface models as a basis for stochastic history match—a Niger delta experience,” in Proceedings of the International Petroleum Technology Conference (IPTC '08), IPTC 12665, Kuala Lumpa, Malaysia, December 2008.
  5. M. D. Morris, “Three technometrics experimental design classics,” Technometrics, vol. 42, no. 1, pp. 26–27, 2000. View at Publisher · View at Google Scholar
  6. G. E. P. Box, W. G. Hunter, and J. S. Hunter, Statistics for Experimenters: Design, Innovation, and Discovery, John Wiley & Sons, New York, NY, USA, 2nd edition, 2005. View at MathSciNet
  7. D. C. Montgomery, Design and Analysis of Experiments: Response Surface Method and Designs, John Wiley & Sons, Hoboken, NJ, USA, 2005.
  8. F. Moeinikia and N. Alizadeh, “Experimental Design in reservoir simulation: an integrated solution for uncertainty analysis, a case study,” Journal of Petroleum Exploration and Production Technology, vol. 2, no. 2, pp. 75–83, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. T. T. Allen, M. A. Bernshteyn, and K. Kabiri-Bamoradian, “Constructing meta-models for computer experiments,” Journal of Quality Technology, vol. 35, no. 3, pp. 264–274, 2003. View at Google Scholar · View at Scopus
  10. V. S. Aigbodion, S. B. Hassan, E. T. Dauda, and R. A. Mohammed, “The development of mathematical model for the prediction of ageing behavior for Al-Cu-Mg/bagasse particulate composite,” Journal of Minerals & Materials Characteristics & Engineering, vol. 9, pp. 907–917, 2010. View at Google Scholar
  11. E. Manceau, M. Mezghani, I. Zabalza-Mezghani, and F. Roggero, “Combination of experimental design and joint modeling methods for quantifying the risk associated with deterministic and stochastic uncertainties—an integrated test study,” in Proceedings of the SPE Annual Technical Conference and Exhibition, SPE 71620, pp. 2537–2547, New Orleans, Lo, USA, October 2001. View at Scopus
  12. B. Yeten, A. Castellini, B. Guyaguler, and W. H. Chen, “A comparison study on experimental design and response surface methodologies,” in Proceedings of the SPE Reservoir Simulation Symposium, vol. 93347, pp. 465–479, Houston, Tex, USA, February 2005. View at Scopus
  13. E. Fetel and G. Caumon, “Reservoir flow uncertainty assessment using response surface constrained by secondary information,” Journal of Petroleum Science and Engineering, vol. 60, no. 3-4, pp. 170–182, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Mohaghegh, “Virtual-intelligence applications in petroleum engineering: part I—artificial neural networks,” Journal of Petroleum Technology, vol. 52, no. 9, pp. 64–73, 2000. View at Google Scholar · View at Scopus
  15. K. K. Salam, D. Araromi, and S. S. Ikiensikimama, “Neuro-fuzzy modeling for the prediction of below-bubble-point viscosity,” Petroleum Science and Technology, vol. 29, no. 17, pp. 1741–1752, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Mu'azu, I. A. Mohammed-Dabo, and S. M. Waziri, “Development of mathematical model for the prediction of essential oil extraction from Eucalyptus citriodora leaves,” Journal of Basic and Applied Scientific Research, vol. 2, no. 3, pp. 2298–2306, 2012. View at Google Scholar
  17. A. Friedmann, D. K. Chawathe, and D. K. Larue, “Assessing uncertainty in channelized reservoirs using experimental designs,” in Proceedings of the SPE Reservoir Evaluation & Engineering, August 2003, SPE 85117.
  18. B. Guyagular, R. N. Horne, L. Rogers, and J. J. Rosenzweig, “Optimization of well placement in a Gulf of Mexico Waterflooding Project,” in Proceedings of the SPE Annual Technical Conference and Exhibition, SPE 63221, Dallas, Tex, USA, October 2001.
  19. J.-P. Dejean and G. Blanc, “Managing uncertainties on production predictions using integrated statistical methods,” in Proceedings of the SPE Annual Technical Conference and Exhibition: ‘Reservoir Engineering’, vol. 56696, Houston, Tex, USA, October 1999. View at Scopus
  20. J. M. Hammersley and D. C. Handscomb, Monte Carlo Methods, Chapman and Hall, London, UK, 1983.