New insights into tracer propagation in partially saturated porous media

Abstract

This work deals with the influence of partial saturation on the transport process of a passive tracer. Transport experiments were done in a water-wet glass micromodel combined with specific optical techniques. Full water saturation was achieved by injecting initially the background solution and then the tracer, whereas for the partial saturation conditions, the micromodel was initially saturated with oil, and then sequential the background solution and the tracer were injected at the same flow rate. We have shown that in the investigated range of water saturations it exists a transition in the oil ganglia structure and size. For high water saturations oil ganglia have one or two pores in size, however for lower water saturations they comprise an important number of pores. Transport strongly depends on the size distribution of the oil ganglia as they create large percolating paths and stagnant zones. We also showed the existence of two different types of stagnant zones: zones accessible by diffusion into pores and zones only accessible by spatially limited diffusion in films. The major advantage of using glass micromodels lies in the fact that dispersion coefficients can be computed from concentrations averaged over the pore space or from concentrations at the outlet and simultaneously from spatial concentration profiles. Curves were fitted using the Advection–Dispersion Equation (ADE) with adequate boundary conditions. The fitting quality of the temporal evolution of the average and outlet concentration was very good. However, fitting of the concentration profiles could only be done for the higher water saturations. This is due to the fact that the Representative Elementary Volume (REV) of lower water saturations is larger than the micromodel. The results show that fitting the breakthrough curve in order to determine the dispersion coefficient in a partially saturated porous medium might be misleading. Indeed, when fitting the breakthrough curves we were able to compute a dispersion coefficient even in the case where the REV of the water saturation is larger than the micromodel. Consequently, the knowledge of the local concentration profiles as a function of time is necessary as it provides an additional information on the spatio-temporal behavior of the transport process and therefor a supplementary constraint of the fitting procedure. Finally, we observed a time dependent dispersion coefficient in the regime where oil ganglia comprise several pores. This fact might be attributed to the non-Gaussian nature of the transport.

Upscaling Mixing-Controlled Reactions in Unsaturated Porous Media

“…Note that the ratio of water in the domain is the porosity, equal for the three scenarios, times the water saturation of the porous phase S w . A decrease in saturation generally leads to a modification of the flow paths, which results in a more heterogeneous velocity field (Leontidis et al 2020;Velásquez-Parra et al 2021). The mixing behaviors in these scenarios are investigated using the numerical simulations and the dispersive lamella approach validated for scenario SW70.…”

“…When two or more fluid phases are present, an additional level of complexity arises (De Gennes 1983). Laboratory experiments in bead packs (Matsubayashi et al 1996), sand columns (Padilla et al 1999;Bromly and Hinz 2004), and recently in milli-and microfluidics experiments (Jiménez-Martínez et al 2015Leontidis et al 2020) have shown that the distribution of the immiscible phase combined with medium heterogeneity alters mixing and dispersion behaviors in the system compared to fluid saturated media. In fact, experimental and numerical studies have highlighted the saturation dependence of dispersion coefficients (Nützmann et al 2002;Raoof and Hassanizadeh 2013;Leontidis et al 2020), and the occurrence of non-Fickian transport (Bromly and Hinz 2004;Zoia et al 2010;Aziz et al 2018;Hasan et al 2019Hasan et al , 2020.…”

“…Laboratory experiments in bead packs (Matsubayashi et al 1996), sand columns (Padilla et al 1999;Bromly and Hinz 2004), and recently in milli-and microfluidics experiments (Jiménez-Martínez et al 2015Leontidis et al 2020) have shown that the distribution of the immiscible phase combined with medium heterogeneity alters mixing and dispersion behaviors in the system compared to fluid saturated media. In fact, experimental and numerical studies have highlighted the saturation dependence of dispersion coefficients (Nützmann et al 2002;Raoof and Hassanizadeh 2013;Leontidis et al 2020), and the occurrence of non-Fickian transport (Bromly and Hinz 2004;Zoia et al 2010;Aziz et al 2018;Hasan et al 2019Hasan et al , 2020. Furthermore, numerical simulations showed that the emergence of highly heterogeneous velocity distributions is related to the development of high-velocity regions (preferential flow paths) and low-velocity regions (stagnation zones) (Triadis et al 2019;Velásquez-Parra et al 2021), which leads to enhanced solute spreading and chemical reactivity (Sole-Mari and Fernàndez-Garcia 2018;Nissan and Berkowitz 2019;Jiménez-Martínez et al 2020).…”

“… 2015 , 2017 ; Leontidis et al. 2020 ) have shown that the distribution of the immiscible phase combined with medium heterogeneity alters mixing and dispersion behaviors in the system compared to fluid saturated media. In fact, experimental and numerical studies have highlighted the saturation dependence of dispersion coefficients (Nützmann et al.…”

“… 2002 ; Raoof and Hassanizadeh 2013 ; Leontidis et al. 2020 ), and the occurrence of non-Fickian transport (Bromly and Hinz 2004 ; Zoia et al. 2010 ; Aziz et al.…”