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Abstract

Summary

Modelling the dynamic fluid behaviour of Low Salinity Water Flooding (LSWF) at the reservoir scale is a challenge which requires a coarse grid enable prediction in a feasible timescale. However, evidence shows that using low resolution models will result in a considerable mismatch compared with an equivalent fine scale model with the potential of strong numerically induced oscillations. This work examines two new upscaling methods in a heterogenous reservoir where viscous crossflow takes place to improve the precision of predictions.

We apply two approaches to upscaling of the flow to improve precision. In the first upscaling method, we shift the effective salinity range for the coarse model based on algorithms that we have developed to correct for numerical dispersion. The second upscaling method uses appropriate pseudo relative permeability curves that we derive. The shape of this new set of relative permeability is designed based on a modified fractional flow analysis of LSWF that we have developed and captures the relationship between dispersion and the waterfront velocities. This approach removes the need for explicit simulation of salinity transport. We applied these approaches in layered models and for permeability distributed as a correlated random field.

Upscaling by shifting the effective salinity range of the coarse model gave a good match to the fine case scenario, while considerable mismatch was observed for traditional upscaling of the absolute permeability only using averaging methods. For highly coarsened models, this method of upscaling reduces the oscillations appear, but they can be apparent. On the other hand, upscaling by using a single (pseudo) relative permeability produced more robust results with a very promising match to the fine scale scenario. These methods of upscaling showed promising results where they were used to upscale fully communicating and non-communicating layers as well as models with randomly correlated permeability.

Unlike documented methods in literate, these newly derived methods take into account the crucial effect of numerical dispersion and effective concentration on fluid dynamic using mathematical tools. These methods could be applied for other models where the phase mobilities change as a result of an injected solute, such as surfactant flooding and alkaline flooding. Usually these models use two sets of relative permeability and switch from one to another as a function of the concentration of the solute.

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2020-09-14
2024-04-19
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References

  1. Al-Ibadi, Hasan, Stephen, K.D. and Mackay, E.
    (2018) ‘Improved Numerical Stability and Upscaling of Low Salinity Water Flooding’, in SPE Asia Pacific Oil and Gas Conference and Exhibition, pp. 23–25. doi: 10.2118/192074‑MS.
    https://doi.org/10.2118/192074-MS [Google Scholar]
  2. Al-Ibadi, H., Stephen, K. D. and Mackay, E.
    (2019a) ‘Insights into the fractional flow of low salinity water flooding in the light of solute dispersion and effective salinity interactions’, Journal of Petroleum Science and Engineering. Elsevier B.V., 174(3), pp. 1236–1248. doi: 10.1016/j.petrol.2018.12.001.
    https://doi.org/10.1016/j.petrol.2018.12.001 [Google Scholar]
  3. Al-Ibadi, H., Stephen, K. and Mackay, E.
    (2019b) ‘Analytical and Numerical Solutions of Chemical Flooding in a Layered Reservoir with a Focus on Low Salinity Water Flooding’, in the 20th European symposium on improved oil recovery.
    [Google Scholar]
  4. Al-Ibadi, H., Stephen, K. D. and Mackay, E.
    (2019c) ‘Analytical Solution of Chemical Flooding in Heterogeneous Non-Communicating Layers with a Focus on Low Salinity Water Flooding’, in the SPE Europec featured at 81st EAGE Annual Conference, p. SPE-195446-MS.
    [Google Scholar]
  5. (2019d) ‘Extended Fractional Flow Model of Low Salinity Water Flooding Accounting for Dispersion and Effective Salinity Range’, SPEJ, 24(06). doi: https://doi.org/10.2118/191222-PA.
    [Google Scholar]
  6. (2019e) ‘Novel Observations of Salinity Transport in Low-Salinity Waterflooding’, Society of Petroleum Engineers, 24(03), pp. 1108–1122.
    [Google Scholar]
  7. (2020) ‘Heterogeneity Effects on Low Salinity Water Flooding’ in the SPE Europec featured at 82nd EAGE Conference and Exhibition.
    [Google Scholar]
  8. Al-Shalabi, E. W. et al.
    (2017) ‘Single-Well Chemical-Tracer Modeling of Low-Salinity-Water Injection in Carbonates’, SPE Reservoir Evaluation & Engineering, 20(01), pp. 118–133. doi: 10.2118/173994‑PA.
    https://doi.org/10.2118/173994-PA [Google Scholar]
  9. Ali, J. A. et al.
    (2019) ‘Low-Salinity Polymeric Nano fluid-Enhanced Oil Recovery Using Green Polymer-Coated ZnO/ SiO 2 Nanocomposites in the Upper Qamchuqa Formation in Kurdistan Region, Iraq’, Energy and Fuels. doi: 10.1021/acs.energyfuels.8b03847.
    https://doi.org/10.1021/acs.energyfuels.8b03847 [Google Scholar]
  10. Fjelde, I., Asen, S. M. and Omekeh, A.
    (2012) ‘Low Salinity Water Flooding Experiments and Interpretation by Simulations’, Eighteenth SPE Improved Oil Recovery Symposium held in Tulsa, (April), pp. 1–12. doi: 10.2118/154142‑MS.
    https://doi.org/10.2118/154142-MS [Google Scholar]
  11. Jackson, M. D., Al-Mahrouqi, D. and Vinogradov, J.
    (2016) ‘Zeta potential in oil-water-carbonate systems and its impact on oil recovery during controlled salinity water-flooding’, Scientific Reports. Nature Publishing Group, 6(October), pp. 1–13. doi: 10.1038/srep37363.
    https://doi.org/10.1038/srep37363 [Google Scholar]
  12. Jadhunandan, P. P. and Morrow, N. R.
    (1995) ‘Effect of Wettability on Waterflood Recovery for Crude-Oil/Brine/Rock Systems’, SPE Reservoir Engineering, 10(1), pp. 40–46. doi: 10.2118/22597‑pa.
    https://doi.org/10.2118/22597-pa [Google Scholar]
  13. Lake, L. W.
    (1989) Enhanced Oil Recovery. Englewood Cliffs, N.J. : Prentice Hall.
    [Google Scholar]
  14. Mykkeltvedt, T. S., Raynaud, X. and Lie, K. A.
    (2017) ‘Fully implicit higher-order schemes applied to polymer flooding’, Computational Geosciences, 21(5–6), pp. 1245–1266. doi: 10.1007/s10596‑017‑9676‑6.
    https://doi.org/10.1007/s10596-017-9676-6 [Google Scholar]
  15. Nasralla, R. A. et al.
    (2018) ‘Low salinity waterflooding for a carbonate reservoir: Experimental evaluation and numerical interpretation’, Journal of Petroleum Science and Engineering. Elsevier B.V., 164(July 2017), pp. 640–654. doi: 10.1016/j.petrol.2018.01.028.
    https://doi.org/10.1016/j.petrol.2018.01.028 [Google Scholar]
  16. Peaceman, D.
    (1977) Fundamentals of numerical reservoir simulation. 6th edn. Houston, Texas, U.S.A.: company, elsevier scientific publishing.
    [Google Scholar]
  17. Schlumberger
    Schlumberger (2018) ‘ECLIPSE Technical Description’, pp. 1–17.
    [Google Scholar]
  18. Shaddel, S. and Tabatabae-Nejad, S.
    a. (2015) ‘Alkali/Surfactant Improved Low-Salinity Waterflooding’, Transport in Porous Media, 106(3), pp. 621–642. doi: 10.1007/s11242‑014‑0417‑1.
    https://doi.org/10.1007/s11242-014-0417-1 [Google Scholar]
  19. Sohrabi, M. et al.
    (2016) ‘Novel Insights Into Mechanisms of Oil Recovery by Use of Low-Salinity-Water Injection’, SPE Journal, 22(02), pp. 407–416. doi: 10.2118/172778‑PA.
    https://doi.org/10.2118/172778-PA [Google Scholar]
  20. Wiegman, L.
    (2017) Numerical aspects of transport modelling in Enhanced Oil Recovery. Delft University ofTechnology.
    [Google Scholar]
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