1887

Abstract

Summary

This paper presents the work of optimising the WAG ratio and slug size in miscible finite sized slug WAG (FSSWAG) injection on a field scale, considering the impact of Todd and Longstaff's mixing parameter (ω, also known as TLMIXPAR) value. This work is an extension of ) work where Al-Haboobi showed there is a relationship between the value of ω with specific slug size and WAG ratio. This relationship was used in the optimisation of slug size and WAG ratio by updating ω (TLMIXPAR) in the Eclipse 100 data deck using a Python code.

In order to identify the impact of the calibrated value of ω on the optimisation of miscible FSS WAG injection, the slug size, WAG ratio, type of fluid injected (so-called WAG pattern injection) and the flow rate were optimised. The optimisation scenario is performed with the assumption that there is an unlimited supply of gas to inject, what if the gas supply was finite (limited)? Therefore, the impact of the calibrated value of ω on the optimisation results has been investigated by adding the assumption that there is a limited amount of gas to inject for the optimisation of WAG ratio, slug size, WAG pattern injection and the amount of flow rate to inject. The results of the previous optimisation scenarios for the calibrated value of ω are compared with the results of the optimisation at a fixed value of ω=1 for both secondary and tertiary recovery. The full details of this work can be found in ( ).

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/content/papers/10.3997/2214-4609.201900127
2019-04-08
2024-04-28
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References

  1. Al-Haboobi, Z. I. M.
    (2019) Calibrating the Todd and Longstaff's mixing parameter value for miscible finite sized slug WAG injection for application and optimisation on a field scale. Doctor of Philosophy Thesis, Heriot-Watt University.
    [Google Scholar]
  2. Alvarado, V. and Manrique, E.
    (2010) ‘Enhanced oil recovery: an update review’, Energies, 3(0), pp. 1529–1575.
    [Google Scholar]
  3. Arnold, D., Demyanov, V., Christie, M., Bakay, A. and Gopa, K.
    (2016) ‘Optimisation of decision making under uncertainty throughout field lifetime: A fractured reservoir example’, Computers and Geosciences, 95, pp. 123–139.
    [Google Scholar]
  4. Arnold, D., Demyanov, V., Tatum, D., Christie, M., Rojas, T., Geiger, S. and Corbett, P.
    (2013) ‘Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation’, Computers and Geosciences, 50, pp. 4–15.
    [Google Scholar]
  5. Awan, A. R., Teigland, R. and Kleppe, J.
    (2008) ‘A survey of North Sea enhanced-oil-recovery projects initiated during the years 1975 to 2005’, SPE Reservoir Evaluation and Engineering, 11(3), pp. 497–512. SPE-99546-PA.
    [Google Scholar]
  6. Bandyopadhyay, S. and Saha, S.
    (2012) Unsupervised classification: similarity measures, classical and metaheuristic approaches, and applications. Springer Science & Business Media.
    [Google Scholar]
  7. Brodie, J. A., Jhaveri, B. S., Moulds, T. P. and Mellemstrand Hetland, S.
    ‘Review of gas injection projects in BP’. Paper SPE-154008-MS presented at the SPE Improved Oil Recovery Symposium (2012): Society of Petroleum Engineers.
    [Google Scholar]
  8. Caetano, R. P. L.
    (2017) Optimization of a water alternating gas injection scheme evaluation of a miscible LPG injection with compositional fluid flow simulation in a Kazakhstan field. Master of Science Thesis, Instituto Superior Técnico, Universidade de Lisboa.
    [Google Scholar]
  9. Christensen, J. R., Stenby, E. H. and Skauge, A.
    (2001) ‘Review of WAG field experience’, SPE Reservoir Evaluation and Engineering, pp. 1–10. SPE-71203-PA.
    [Google Scholar]
  10. Christie, M., Eydinov, D., Demyanov, V., Talbot, J., Arnold, D. and Shelkov, V.
    ‘Use of multi-objective algorithms in history matching of a real field’. Paper SPE-163580-MS presented at the SPE reservoir simulation symposium (2013): Society of Petroleum Engineers
    [Google Scholar]
  11. Crogh, N. A., Eide, K. and Morterud, S. E.
    ‘WAG injection at the Statfjord field, a success story’. Paper SPE-78348-MS presented at the European Petroleum Conference (2002): Society of Petroleum Engineers.
    [Google Scholar]
  12. Erbas, D., Dunning, M., Nash, T. M., Cox, D., Stripe, J. A. and Duncan, E.
    ‘Magnus WAG pattern optimization through data integration’. Paper SPE-169167-MS presented at the SPE improved oil recovery symposium (2014): Society of Petroleum Engineers.
    [Google Scholar]
  13. Fayers, F. J., Blunt, M. J. and Christie, M. A.
    ‘Accurate calibration of empirical viscous fingering models’. ECMOR II-2nd European Conference on the Mathematics of Oil Recovery 1990, 1990.
    [Google Scholar]
  14. Fernández Martínez, J. L., Mukerji, T., García Gonzalo, E. and Suman, A.
    (2012) ‘Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers’, Geophysics, 77(1), pp. M1-M16.
    [Google Scholar]
  15. Fonseca, C. M., Fleming, P. J., Zitzler, E., Deb, K. and Thiele, L.
    ‘Evolutionary multi-criterion optimization’. In Second International Conference, EMO 2003.
    [Google Scholar]
  16. Geoquest, S.
    2014. Eclipse 100 reference manual. Schlumberger Geoquest.
    [Google Scholar]
  17. Hassan, R., Cohanim, B., de Weck, O. and Venter, G.
    (2004) ‘A copmarison of particle swarm optimization and the genetic algorithm’, American Institute of Aeronautics and Astronautics.
    [Google Scholar]
  18. Hutahaean, J.
    (2017) Multi-objective methods for history matching, uncertainty prediction and optimisation in reservoir modelling. Doctor of Philosophy Thesis, Heriot-Watt University.
    [Google Scholar]
  19. Irgens, M. and Lavenue, W. L.
    ‘Use of advanced optimization techniques to manage a complex drilling schedule’. Paper SPE-110805-MS presented at the SPE Annual Technical Conference and Exhibition (2007): Society of Petroleum Engineers.
    [Google Scholar]
  20. Jhaveri, B. S., Brodie, J. A., Zhang, P. and Daae, V.
    ‘Review of BP's global gas injection projects’. Paper SPE-171780-MS presented at the 21st World Petroleum Congress (2014): World Petroleum Congress.
    [Google Scholar]
  21. John, F. O.
    (2015) Optimization of a water alternating gas injection compositional fluid flow simulation with water alternating gas injection optimization on the upscaled synthetic reservoir CERENA-I. Master of Science Thesis, Instituto Superior Técnico, Universidade de Lisboa.
    [Google Scholar]
  22. Kang, P. S., Lim, J. S. and Huh, C.
    (2016) ‘Screening criteria and considerations of offshore enhanced oil recovery’, Energies, 9(1), pp. 44.
    [Google Scholar]
  23. Kathrada, M.
    (2009) Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering. Doctor of Philosophy Thesis, Heriot-Watt University.
    [Google Scholar]
  24. Kennedy, J. and Eberhart, R.
    ‘Particle swarm optimization’. Neural Networks, 1995. Proceedings., IEEE International Conference on, Nov/Dec 1995, 1942-1948 vol.4.
    [Google Scholar]
  25. Khu, S. T. and Madsen, H.
    (2005) ‘Multiobjective calibration with Pareto preference ordering: an application to rainfall-runoff model calibration’, Water Resources Research, 41(3).
    [Google Scholar]
  26. Lepot, M., Aubin, J. B. and Clemens, F. H.
    (2017) ‘Interpolation in time series: an introductive overview of existing methods, their performance criteria and uncertainty assessment’, Water, 9(10).
    [Google Scholar]
  27. Mohagheghian, E.
    (2016) An application of evolutionary algorithms for WAG optimisation in the Norne Field. Doctor of Philosophy Thesis, Memorial University of Newfoundland.
    [Google Scholar]
  28. Mohamed, L. M. Y.
    (2011) Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction.Doctor of Philosophy Thesis, Heriot-Watt University.
    [Google Scholar]
  29. Morais, H. L.
    (2012) Application of WAG and SWAG injection techniques in Norne E-segment. Master of Science Thesis, Norwegian University of Science and Technology [Online] Available at: http://www.diva-portal.org/smash/get/diva2:589680/FULLTEXT01.pdf (Accessed.
    [Google Scholar]
  30. Onwunalu, J. E. and Durlofsky, L. J.
    (2010) ‘Application of a particle swarm optimization algorithm for determining optimum well location and type’, Computational Geosciences, 14(1), pp. 183–198.
    [Google Scholar]
  31. Self, R. V., Atashnezhad, A. and Hareland, G.
    ‘Use of a Swarm Algorithm to Reduce the Drilling Time through Measurable Improvement in Rate of Penetration’. 50th US Rock Mechanics/Geomechanics Symposium2016: American Rock Mechanics Association.
    [Google Scholar]
  32. Shpak, R.
    (2013) Modeling of miscible WAG injection using real geological field data. Norwegian University of Science and Technology, Norway [Online] Available at: http://www.diva-portal.org/smash/get/diva2:644970/FULLTEXT01.pdf (Accessed.
    [Google Scholar]
  33. Todd, M. R. and Longstaff, W. J.
    (1972) ‘The development, testing, and application of a numerical simulator for predicting miscible flood performance’, Journal of Petroleum Technology, 24(7), pp. 874–882. SPE-3484-PA
    [Google Scholar]
  34. Vazquez, O., Young, C., Demyanov, V., Arnold, D., Fisher, A., MacMillan, A. and Christie, M.
    (2015) ‘Produced-water-chemistry history matching in the Janice field’, SPE Reservoir Evaluation and Engineering, 18(04), pp. 564–576. SPE-164903-PA.
    [Google Scholar]
  35. Wilson, A.
    (2015) ‘Magnus water alternating gas pattern optimization through data integration’, Journal of Petroleum Technology67(6), pp. 117-119. SPE-0615-0117-JPT.
    [Google Scholar]
  36. Zahoor, M. K., Derahman, M. N. and Yunan, M. H.
    (2011) ‘WAG process design–an update review’, Brazilian Journal of Petroleum and Gas, 5(2), pp. 109–121.
    [Google Scholar]
  37. Zhang, P., Brodie, J. A., Daae, V., Erbas, D. and Duncan, E.
    ‘BP North Sea Miscible Gas Injection Projects Review’. Paper SPE-166597-MS presented at the SPE Offshore Europe Oil and Gas Conference and Exhibition (2013): Society of Petroleum Engineers.
    [Google Scholar]
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