1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. IOR 2021
  5. Conference paper

Abstract

Summary

Due to the complexity of underlying physics of the modified salinity water flooding, mechanistic models are often utilized to better understand and predict its behaviour in the field scale. The mechanistic models are a combination of several submodels of different nature, each with several adjustable parameters. Adjusting these parameters by fitting the model to a limited number of recovery factors obtained from core flooding experiments is not a viable solution, often estimating highly uncertain values for the model parameters. We address this challenge by providing a framework for fitting a mechanistic two-phase reactive-transport model to a combination of low-cost single- and two-phase flow experimental data.

The effectiveness of modified-salinity water flooding is often tested in (qualitative) spontaneous and (quantitative) forced imbibition tests in the lab. Several mechanisms that are suggested for explaining the observed improved oil recovery cannot be distinguished in those traditional imbibition tests. Mechanistic models with adjustable physically-meaningful parameters exist for these mechanisms, e.g. carbonate dissolution, surface charge (and force) alteration, fines migration, water weakening, etc. However, obtaining these adjustable parameters by fitting “a mechanistic model that incorporates all these mechanisms” is not a good strategy. Our mechanistic models are a combination of chemical equilibrium and kinetics models that describe the chemical reactions between the ionic species in the aqueous phase (electrolyte model with known parameters), chalk and oil surface complexation reactions (CD-MUSIC, diffuse layer models with unknown parameters), and an empirical parameter linking the surface reactions to the relative permeability and capillary pressure model parameters. When fitting the model to the core flooding data, the optimization algorithms are more sensitive to the relative permeability parameters. Moreover, the number of parameters are often too many and can result in a largely underdetermined system of equations, for which the optimization algorithm is very sensitive to the initial estimates.

Our novel numerical framework optimizes the model parameters by simultaneously fitting the parameters to a set of core flooding, spontaneous imbibition, and single-phase chromatographic tests (i.e. injecting MSW to a core saturate with formation water and measuring effluent ionic concentrations with time). We obtain the initial estimates of the surface complexation model parameters by fitting the model to the zeta potential measurements performed on powdered carbonate suspensions. We finally demonstrate the capabilities of our framework by optimizing model parameters for a set of inhouse experiments performed on chalk cores from the North Sea reservoirs.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202133091
2021-04-19
2024-04-27

  • From This Site

    /content/papers/10.3997/2214-4609.202133091
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Al-Shalabi, E.W., Sepehrnoori, K., Pope, G. et al.
    [2015] Mechanistic modeling of oil recovery due to low salinity water injection in oil reservoirs. In: SPE Middle East Oil & Gas Show and Conference. Society of Petroleum Engineers, –.
     [Google Scholar]
  2. Bonto, M., Eftekhari, A.A. and M.Nick, H.
    [2020] Wettability Indicator Parameter Based on the Ther-modynamic Modeling of Chalk-Oil-Brine Systems. Energy & Fuels, 34(7), 8018–8036.
     [Google Scholar]
  3. Bonto, M., Eftekhari, A.A. and Nick, H.
    [2019] A calibrated model for the carbonate-brine-crude oil surface chemistry and its effect on the rock wettability, dissolution, and mechanical properties. In: SPE Reservoir Simulation Conference. OnePetro, –.
     [Google Scholar]
  4. Dang, C., Nghiem, L., Nguyen, N., Chen, Z. and Nguyen, Q.
    [2016] Mechanistic modeling of low salinity water flooding. J. Pet. Sci. Eng., 146, 191–209.
     [Google Scholar]
  5. Eftekhari, A.A.
    [2019] PhreeqcMatlab: a Matlab wrapper for the PhreeqcRM C interface.
     [Google Scholar]
  6. Eftekhari, A.A., Schüller, K., Planella, F.B. and Werts, M.
    [2015] FVTool: a finite volume toolbox for Matlab.
     [Google Scholar]
  7. Eftekhari, A.A., Thomsen, K., Stenby, E.H. and Nick, H.M.
    [2017] Thermodynamic Analysis of Chalk-Brine-Oil Interactions. Energy and Fuels, 31(11), 11773–11782.
     [Google Scholar]
  8. Evje, S. and Hiorth, A.
    [2011] A model for interpretation of brine-dependent spontaneous imbibition experiments. Adv. Water Resour., 34(12), 1627–1642.
     [Google Scholar]
  9. Fathi, S.J., Austad, T. and Strand, S.
    [2010] “Smart water” as a wettability modifier in chalk: the effect of salinity and ionic composition. Energy & fuels, 24(4), 2514–2519.
     [Google Scholar]
  10. Goldberg, S.
    [2013] Surface Complexation Modeling. In: Reference Module in Earth Systems and Environmental Sciences, Elsevier, 1–14.
     [Google Scholar]
  11. Katende, A. and Sagala, F.
    [2019] A critical review of low salinity water flooding: mechanism, laboratory and field application. Journal of Molecular Liquids, 278, 627–649.
     [Google Scholar]
  12. Korrani, A.K. and Jerauld, G.R.
    [2019] Modeling wettability change in sandstones and carbonates using a surface-complexation-based method. J. Pet. Sci. Eng., 174(December 2018), 1093–1112.
     [Google Scholar]
  13. Ligthelm, D.J., Gronsveld, J., Hofman, J., Brussee, N., Marcelis, F., van der Linde, H. et al.
    [2009] Novel waterflooding strategy by manipulation of injection brine composition. In: EUROPEC/EAGE conference and exhibition. Society of Petroleum Engineers, –.
     [Google Scholar]
  14. Loeve, D., Wilschut, F., Hanea, R.H., Maas, J., Hooff, P.M.E., den Hoek, P., Douma, S.G. and Doren, J.F.M.
    [2011] Simultaneous Determination of Relative Permeability and Capillary Pressure Curves by Assisted History Matching Several SCAL Experiments. In: Soc. Core Anal. Conf. Pap. SCA2011-35. 1–12.
     [Google Scholar]
  15. Nia Korrani, A.K., Sepehrnoori, K. and Delshad, M.
    [2013] A novel mechanistic approach for modeling low salinity water injection. SPE Annu. Tech. Conf. Proc., 7(1999), 3206–3223.
     [Google Scholar]
  16. Nick, H.M., Raoof, A., Centler, F., Thullner, M. and Regnier, P.
    [2013] Reactive dispersive contaminant transport in coastal aquifers: Numerical simulation of a reactive Henry problem. J. Contam. Hydrol., 145, 90–104.
     [Google Scholar]
  17. Parkhurst, D.L. and Wissmeier, L.
    [2015] PhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQC. Advances in Water Resources, 83, 176–189.
     [Google Scholar]
  18. Qiao, C., Johns, R., Li, L. and Xu, J.
    [2015] Modeling low salinity waterflooding in mineralogically different carbonates. Proc. - SPE Annu. Tech. Conf. Exhib., 2015-January, 4168–4187.
     [Google Scholar]
  19. Rezaei Doust, A., Puntervold, T., Strand, S. and Austad, T.
    [2009] Smart water as wettability modifier in carbonate and sandstone: A discussion of similarities/differences in the chemical mechanisms. Energy & fuels, 23(9), 4479–4485.
     [Google Scholar]
  20. Saw, R.K. and Mandal, A.
    [2020] A mechanistic investigation of low salinity water flooding coupled with ion tuning for enhanced oil recovery. RSC Advances, 10(69), 42570–42583.
     [Google Scholar]
  21. Seccombe, J., Lager, A., Webb, K., Jerauld, G. and Fueg, E.
    [2008] Improving waterflood recovery: LoSal EOR field evaluation, SPE. In: DOE Symposium on Improved Oil Recovery (Tulsa, Oklahoma, USA), SPE, 113480. –.
     [Google Scholar]
  22. Song, J., Zeng, Y., Wang, L., Duan, X., Puerto, M., Chapman, W.G., Biswal, S.L. and Hirasaki, G.J.
    [2017] Surface complexation modeling of calcite zeta potential measurements in brines with mixed potential determining ions (Ca2+,CO32−,Mg2+,SO42−) for characterizing carbonate wettability. J. Colloid Interface Sci., 506, 169–179.
     [Google Scholar]
  23. Sun, Y., Petersen, J.N. and Clement, T.P.
    [1999] Analytical solutions for multiple species reactive transport in multiple dimensions. Journal of Contaminant Hydrology, 35(4), 429–440.
     [Google Scholar]
  24. Taheri, M., Bonto, M., Eftekhari, A.A., M Nick, H. et al.
    [2019] Towards Identifying the Mechanisms of the Modified-Salinity Waterflooding by a Novel Combination of Core flooding and Mathematical Modeling. In: SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers, 1–30.
     [Google Scholar]
  25. Taheriotaghsara, M., Bonto, M., Eftekhari, A.A. and Nick, H.M.
    [2020] Prediction of oil breakthrough time in modified salinity water flooding in carbonate cores. Fuel, 274(January), 117806.
     [Google Scholar]
  26. Webb, K., Black, C., and Al-Ajeel, H.
    [2004] Low salinity oil recovery-log-inject-log. In: SPE/DOE Symposium on Improved Oil Recovery. OnePetro, –.
     [Google Scholar]
  27. Yu, L., Evje, S., Kleppe, H., Kårstad, T., Fjelde, I. and Skjaeveland, S.M.
    [2009] Spontaneous imbibition of seawater into preferentially oil-wet chalk cores - Experiments and simulations. J. Pet. Sci. Eng., 66(3-4), 171–179.
     [Google Scholar]
  28. Yu, L., Kleppe, H., Kaarstad, T., Skjaeveland, S.M., Evje, S. and Fjelde, I.
    [2008] Modelling of wettability alteration processes in carbonate oil reservoirs. Networks Heterog. Media, 3(1), 149–183.
     [Google Scholar]
  29. Zhang, P. and Austad, T.
    [2006] Wettability and oil recovery from carbonates: Effects of temperature and potential determining ions. Colloids Surfaces A Physicochem. Eng. Asp., 279(1-3), 179–187.
     [Google Scholar]
  30. Zhang, P., Tweheyo, M.T. and Austad, T.
    [2007] Wettability alteration and improved oil recovery by spontaneous imbibition of seawater into chalk: Impact of the potential determining ions Ca2+, Mg2+, and SO42-. Colloids Surfaces A Physicochem. Eng. Asp., 301(1-3), 199–208.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202133091
Loading
Estimating two-phase reactive flow model parameters from single- and two-phase modified-salinity core flooding data
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202133091
/content/papers/10.3997/2214-4609.202133091
/content/papers/10.3997/2214-4609.202133091
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error