1887

Abstract

Summary

The outcome of immiscible enhanced oil recovery (EOR) processes such as low salinity water injection and polymer flooding can be very sensitive to geological heterogeneity. In some cases the resulting reduction in macroscopic sweep (versus a waterflood) can be more than the improvement in microscopic displacement efficiency. It is therefore important to be able to model the impact of this heterogeneity during simulation studies. This may require an upscaling step if the geological heterogeneity is smaller than the simulation grid block size or simply to compensate for numerical diffusion on the coarse grid.

This paper proposes an upscaling methodology that can be applied to different EOR processes and demonstrates its application to low salinity water injection examples. The methodology involves a hierarchy of upscaling steps. First the absolute permeability is upscaled with the objective of predicting the correct changes in pressure. This may also involve near well bore upscaling. Next pseudo relative permeability curves are generated to capture the shock front behaviour. In this study we compare results obtained from using traditional pore volume weighted pseudos with those obtained using pseudos determined analytically using Buckley-Leverett theory. Finally the simulator models relevant to the EOR process of interest are upscaled. In low salinity waterflooding this is the choice of low and high salinity thresholds.

The upscaled models are in better agreement with the fine grid models in terms of pressure, water saturation production and production of either salinity or polymer than are the outputs of coarse grid models without upscaling. The results using the analytical pseudo relative permeabilities are comparable to those obtained from simulations using the pore volume weighted pseudo in many of the cases tested. These models are obviously less time-consuming and complex to generate as they do not need the engineer to run a full fine grid simulation of the EOR process to calculate the pseudo relative permeabilities.

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2014-09-08
2024-03-29
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References

  1. Alsofi, A.M., Blunt, M.J.
    [2013] Control of numerical dispersion in streamline-based simulations of augmented waterflooding. SPE Journal, 18(6), 1102–1111. SPE-129658.
    [Google Scholar]
  2. Arya, A., Hewett, T.A., Larson, R.G. and LakeL.W.
    [1988] Dispersion and reservoir heterogeneity. SPE Reservoir Engineering, 3(1), 139–148. SPE-14364.
    [Google Scholar]
  3. Babaei, M. and King, P.
    [2013] An upscaling-static downscaling scheme for simulation of enhanced oil recovery processes. Transport in Porous Media, 98(2), 465–484.
    [Google Scholar]
  4. Barker, J.W. and Dupouy, P.
    [1999] An analysis of dynamic pseudo- relative permeability methods for oil-water flows. Petroleum Geoscience, 5(4), 385–394.
    [Google Scholar]
  5. Barker, J.W. and Fayers, F.J.
    [1994] Transport coefficients for compositional simulation with coarse grids in heterogeneous media. SPE Advanced Technology Series, 2(2), 103–112. SPE 22591.
    [Google Scholar]
  6. Barker, J.W. and Thibeau, S.
    [1997] A critical review of the use of pseudorelative permeabilities for upscaling. SPE Reservoir Engineering, 12(2), 138–143.
    [Google Scholar]
  7. Buckley, S.E. and Leverett, M.C.
    [1942] Mechanisms of fluid displacements in sands. Trans. AIMME, 146, 107.
    [Google Scholar]
  8. Christie, M.A. and Blunt, M.J.
    [2001] 10th SPE Comparative solution project: a comparison of upscaling echnqiues. SPE Reservoir Engineering and Evaluation, 4(4), 308–317. SPE-72469.
    [Google Scholar]
  9. Dake, L.
    [1971] Fundamentals of reservoir engineering. Elsevier Science.
    [Google Scholar]
  10. Ding, Y. and Renard, G.
    [1994] A new representation of wells in numerical reservoir simulation. SPE Reservoir Engineering, 9(2), 140–144.
    [Google Scholar]
  11. Fayers, F.J.
    [1988] An approximate model with physically interpretable parameters for representing miscible viscous fingering. SPE Journal, 3(2), 551–558. SPE-13166.
    [Google Scholar]
  12. Hewett, T.A., Suzuki, K. and Christie, M.A.
    [1998] Analytical calculation of coarse-grid corrections for use in pseudofunctions. SPE Journal, 3(3), 293–304. SPE-51269.
    [Google Scholar]
  13. Jerauld, G.R., Webb, K.J, Lin, C-Y. and Seccombe, J.C.
    [2008] Modeling low salinity waterflooding. SPE Reservoir Evaluation and Engineering, 11(6), 1000–1012. SPE-102239.
    [Google Scholar]
  14. Jones, A., Doyle, J., Jacobsen, T. and Kjonsvik, D.
    [1995] Which sub-seismic heterogeneities influence waterflood performance? A case study of a low net-to-gross fluvial reservoir. Geological Society, London, Special Publications, 84, 18–.
    [Google Scholar]
  15. Kjonsvik, D., Doyle, J., Jacobsen, T. and Jones, A.
    [1994] The effects of sedimentary heterogeneities on production from a shallow marine reservoir- what really matters?SPE 28445 presented at the European Petroleum Conference, London, UK, 25–27 October.
    [Google Scholar]
  16. Koval, E.J.
    [1963] A method for predicting the performance of unstable miscible dislacement in heterogeneous media. SPE Journal, 3(2), 145–154. SPE-450.
    [Google Scholar]
  17. Kyte, J.R. and Berry, D.W.
    [1975] New pseudo functions to control numerical dispersion. SPE Journal, 15(4), 269–276. SPE-5105.
    [Google Scholar]
  18. Lake, L.W. and Hirasaki, G.J.
    [1981] Taylor’s dispersion in stratified porous media. SPE Journal, 21(4), 459–468. SPE-8436-PA.
    [Google Scholar]
  19. Lantz, R.B.
    [1971] Quantitative evaluation of numerical diffusion (truncation error). SPE Journal, 11(3), 315–320. SPE-2811.
    [Google Scholar]
  20. Mahadevan, J., Lake, L.W. and John, R.T.
    [2002] Estimation of true dispersivity in field scale permeable media. SPE Journal, 8(3), 272–279. SPE 75247.
    [Google Scholar]
  21. Mahani, H., Muggeridge, A.H. and Ashjari, M.A.
    [2009] Vorticity as a measure of heterogeneity for improving coarse grid generation. Petroleum Geoscience, 15(1), 91–102.
    [Google Scholar]
  22. Muggeridge, A.H.
    [1991] Generation of pseudo relative permeabilities from detailed simulation of flow in heterogeneous porous media. In: Lake, L.W., CarrollH.B.Jr., and Wesson, T.C. (Eds) Reservoir Characterization II. San Diego, California, Academic Press.
    [Google Scholar]
  23. Perkins, T.K. and Johnston, O.C.
    [1963] A review of diffusion and dispersion in porous media. SPE Journal, 3(1), 70–84. SPE-480.
    [Google Scholar]
  24. Pope, G.A.
    [1980] The application of fractional flow theory to enhanced oil recovery. SPE Journal, 20(3), 191–205. SPE-7660.
    [Google Scholar]
  25. Renard, Ph. and De Marsily, G.
    [1997] Calculating equivalent permeability: a review. Advances in Water Resources, 20, 278–.
    [Google Scholar]
  26. Smith, E.H.
    [1991] The influence of small-scale heterogeneity on average relative permeability. In: Lake, L.W., CarrollH.B.Jr., and Wesson, T.C. (Eds) Reservoir Characterization II. San Diego, California, Academic Press.
    [Google Scholar]
  27. 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), 874–882. SPE 3484.
    [Google Scholar]
  28. Welge, H.J.
    [1952] A simplified method for computing oil recovery by gas or water drive. Trans. AIMME, 195, 91-.
    [Google Scholar]
  29. Wheat, M.R. and Dawe, R.A.
    [1988] Transverse dispersion in slug-mode chemical EOR processes in stratified porous media. SPE Reservoir Engineering, 3(2), 466–478. SPE-14890.
    [Google Scholar]
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