Abstract
The objective of this work is to develop and apply a numerical workflow for the simultaneous determinations of the effective porosity and the dispersion coefficient from core flood taking chemical reactions into account. This approach consists of modeling the species transport at the core scale coupled with the chemical reactions, as involved in alkali flooding core experiments. Then, the one-dimensional model is compared with experimental breakthrough curves of tracer and pH, and the optimization process based on a genetic algorithm permits to identify the optimal parameter combination of effective porosity and dispersion coefficient. This approach is validated for an artificial case, then investigated on two distinct experimental configurations of core flooding. The proposed workflow, which involves an additional experimental measurement for pH, provides both an estimate of the kinetic porosity that is lower than the measured porosity, as well as an estimate of the dispersion coefficient that is significantly different from the usual single signal approach. The tests performed in this work do not take the interactions of the fluid with the rock into account.
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References
Andersen, P., Evje, S., Madland, M.V., Hiorth, A.: A geochemical model for interpretation of chalk core flooding experiments. Chem. Eng. Sci. 84, 218–241 (2012). https://doi.org/10.1016/j.ces.2012.08.038
Bear, J.: Dynamics of fluids in porous media. Elsevier, Amsterdam (1972)
Bertaux, J., Lemanczyk, Z.R., Schlumberger, D.: Importance of dissolution/precipitation mechanisms in sandstone-alkali interactions. SPE Reservoir Engineering (1987)
Chen, H., Gao, B., Li, H., Ma, L.Q.: Effects of pH and ionic strength on sulfamethoxazole and ciprofloxacin transport in saturated porous media. J. Contam. Hydrol. 126(1–2), 29–36 (2011). https://doi.org/10.1016/j.jconhyd.2011.06.002
Dentz, M., Le Borgne, T., Englert, A., Bijeljic, B.: Mixing, spreading and reaction in heterogeneous media: a brief review. J. Contam. Hydrol. 120–121, 1–17 (2011). https://doi.org/10.1016/j.jconhyd.2010.05.002
Fathi, S.J., Austad, T., Strand, S.: Water-based enhanced oil recovery (EOR) by “‘smart water’”: optimal ionic composition for EOR in carbonates. Energy Fuels 25(11), 5173–5179 (2011). https://doi.org/10.1021/ef201019k
Fried, J.J., Combarnous, M.A.: Dispersion in porous media. Adv. Hydrosci. 7, 169–282 (1971)
Guo, H., Li, Y., Wang, F., Gu, Y.: Comparison of strong-alkali and weak-alkali ASP-flooding field tests in Daqing oil field. SPE Prod. Oper. 33(2), 353–362 (2018). https://doi.org/10.2118/179661-pa
Hunter, K., McInnis, L., Ellis-Toddington, T., Energy, H., Kerr, S.: The use of modeling and monitoring to control scale in Alberta ASP floods. In SPE Enhanced Oil Recovery Conference, pages 735–740, 2013. https://doi.org/10.2118/165285-ms
Kamrani, S., Rezaei, M., Kord, M., Baalousha, M.: Transport and retention of carbon dots (CDs) in saturated and unsaturated porous media: Role of ionic strength, pH, and collector grain size. Water Res. 133, 338–347 (2018). https://doi.org/10.1016/j.watres.2017.08.045
Kurganov, A., Tadmor, E.: New high-resolution central schemes for nonlinear conservation laws and convection-diffusion equations. J. Comput. Phys. 160(1), 241–282 (2000). https://doi.org/10.1006/jcph.2000.6459
Labrid, J., Bazin, B.: Flow modeling of alkaline dissolution by a thermodynamic or by a kinetic approach. SPE Reserv. Eng. 8(02), 151–159 (1993). https://doi.org/10.2118/21082-pa
Leal, A.M.M., Kulik, D.A., Kosakowski, G.: Computational methods for reactive transport modeling: a Gibbs energy minimization approach for multiphase equilibrium calculations. Adv. Water Resour. 88, 231–240 (2016a). https://doi.org/10.1016/j.advwatres.2015.11.021
Leal, A.M.M., Kulik, D.A., Saar, M.O.: Enabling Gibbs energy minimization algorithms to use equilibrium constants of reactions in multiphase equilibrium calculations. Chem. Geol. 437, 170–181 (2016b). https://doi.org/10.1016/j.chemgeo.2016.05.029
Luersen, M.A., Le Riche, R.: Globalized Nelder-Mead method for engineering optimization. Comput. Struct. 82(23–26), 2251–2260 (2004). https://doi.org/10.1016/j.compstruc.2004.03.072
Mohnot, S.M.: A study of mineral/alkali reactions. SPE Reserv. Eng. 2(4), 653–663 (1987)
Musvoto, E.V., Wentzel, M.C.M., Ekama, G.A.M.: Integrated chemical-physical processes modelling II. Simulating aeration treatment of anaerobic digester supernatants. Water 34(6), 1868–1880 (2000a)
Musvoto, E.V., Wentzel, M.C.M., Loewenthal, R.E., Ekama, G.A.M.: Integrated chemical-physical processes modelling I. Development of a kinetic-based model for mixed weak acid/base systems. Water Res. 34(6), 1857–1857 (2000b)
Oliveira, T.D.S., Blunt, M.J., Bijeljic, B.: Modelling of multispecies reactive transport on pore-space images. Adv. Water Resour. (2019). https://doi.org/10.1016/j.advwatres.2019.03.012
Panthi, K., Clemens, T., Mohanty, K.K.: Development of an ASP formulation for a sandstone reservoir with divalent cations. J. Pet. Sci. Eng. 145, 382–391 (2016). https://doi.org/10.1016/j.petrol.2016.05.034
Pini, R., Benson, S.M.: Simultaneous determination of capillary pressure and relative permeability curves from core-flooding experiments with various fluid pairs. Water Resour. Res. 49(6), 3516–3530 (2013). https://doi.org/10.1002/wrcr.20274
Schumi, B., Clemens, T., Wegner, J., Ganzer, L., Kaiser, A., Hincapie, R.E., Leitenmueller, V.: Alkali/cosolvent/polymer flooding of high-TAN oil: using phase experiments, micromodels, and corefloods for injection-agent selection. SPE Reserv. Eval. Eng. 23(02), 463–478 (2020). https://doi.org/10.2118/195504-pa
Sivasankar, P., Suresh Kumar, G., Mathematical modelling and numerical simulation: Influence of pH on dynamics of microbial enhanced oil recovery processes using biosurfactant producing Pseudomonas putida. Bioresour. Technol. 224, 498–508 (2017). https://doi.org/10.1016/j.biortech.2016.10.091
Southwick, J.G.: Solubility of silica in alkaline solutions: implications for alkaline flooding. SPE J. 25(6), 857–864 (1985). https://doi.org/10.2118/12771-pa
Sprocati, R., Masi, M., Muniruzzaman, M., Rolle, M.: Modeling electrokinetic transport and biogeochemical reactions in porous media: a multidimensional Nernst-Planck-Poisson approach with PHREEQC coupling. Adv. Water Resour. (2019). https://doi.org/10.1016/j.advwatres.2019.03.011
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimisation over continuous spaces. J. Glob. Optim. 11, 341–359 (1997). https://doi.org/10.1071/ap09004
Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey CJ: Ä. Polat, Yu Feng, Eric W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, : SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17(3), 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2
Wales, D.J., Doye, J.P.K.: Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J. Phys. Chem. A 101(28), 5111–5116 (1997). https://doi.org/10.1021/jp970984n
Wormington, M., Panaccione, C., Matney, K.M., Bowen, D.K.: Characterization of structures from X-ray scattering data using genetic algorithms. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 357(1761), 2827–2848 (1999). https://doi.org/10.1098/rsta.1999.0469
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Guibert, R., Horgue, P., Schumi, B. et al. Simultaneous Determinations of Effective Porosity and Dispersion Coefficient from Core Flooding Experiments, Considering Chemical Reactions. Transp Porous Med 140, 837–850 (2021). https://doi.org/10.1007/s11242-021-01651-w
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DOI: https://doi.org/10.1007/s11242-021-01651-w