An aqueous nanoparticle dispersion is a complex fluid whose mobility in porous media is controlled by four key factors: the conditions necessary for the stability of nanoparticle dispersions, the kinetics of nanoparticle aggregation in an unstable suspension, the rheology of stable or unstable suspensions, and the interactions between the nanoparticles and oil/water interface and mineral surfaces. The challenges in controlling nanoparticle transport come from the variations of pH and ionic strength of brine, the presence of stationary and mobile phases (minerals, oil, water and gas), the geochemical complexity of reservoir rocks, and pore-network. The overall objective of this work is to achieve a better understanding of nanoparticle transport in porous media based on a systematic experimental and theoretical study of above factors. For this purpose, the critical conditions for the aqueous stability of nanoparticles are identified and fit by a theoretical model, which describes the interaction energy between silica nanoparticles. Above critical conditions nanoparticle aggregation becomes significant. A model for the aggregation kinetics is developed and validated by experiments. A mechanistic model for predicting the viscosity of stable and unstable silica nanoparticle dispersions over a wide range of solid volume fraction is developed. This model is based on the concept of effective maximum packing fraction. Adsorption experiments with silica nanoparticles onto quartz, calcite and clay surfaces and interfacial tension measurements provide insightful information on the interaction of the nanoparticles with minerals and decane/water interface. The extent of nanoparticle adsorption on mineral/water and decane/water interfaces is evaluated based on DLVO theory and Gibbs’ equation. Visual observations and analytical methods are used to understand the interaction of nanoparticles with clay. The characterization of nanoparticle behavior in bulk phases is built into an understanding of nanoparticle transport in porous media. In particular, the rheology of nanoparticle dispersions flowing through permeable media is compared with those determined using a rheometer. In the presence of residual oil, the retention of silica nanoparticles at water/oil interface during steady flow is investigated. The results from batch experiments of nanoparticle adsorption are used to explain the flow behavior of these nanoparticles in a glass bead pack at residual oil saturation. / text

Accurate prediction of colloid deposition rates in porous media is essential in several applications. These include natural filtration of pathogenic microorganisms such as bacteria, viruses, and protozoa, transport and fate of colloid-associated transport of contaminants, deep bed and river bank filtration for water treatment, fate and transport of engineered nanoparticles released into the environment, and bioremediation of contaminated sites. Colloid transport in porous media is a multi-scale problem, with length scales spanning from the sub-pore scale, where the particle-soil interaction forces control the deposition, up to the Darcy scale, where the macroscopic equations governing particle transport are formulated. Colloid retention at the Darcy scale is due to the lumped effect of processes occurring at the pore scale. This requires the incorporation of the micro-scale physics into macroscopic models for a better understanding of colloid deposition in porous media. That can be achieved through pore-scale modeling and the subsequent upscaling to the Darcy scale. Colloid Filtration Theory (CFT), the most commonly used approach to describe colloid attachment onto the soil grains in the subsurface, is found to accurately predict the deposition rates of micron-sized particles under favorable conditions for deposition. But, CFT has been found to over predict particle deposition rates at low flow velocity conditions, typical of groundwater flow, and for nanoscale particles. Also, CFT is found to be inapplicable at typical environmental conditions, where conditions become unfavorable for deposition, due to factors not considered in CFT such as deposition in the secondary minimum of the interaction energy profile, grain surface roughness, surface charge heterogeneity of grains and colloids, and deposition at grain-to-grain contacts. To the best of our knowledge, mechanistic-based models for predicting colloid deposition rates under unfavorable conditions do not exist. Currently, fitting the colloid breakthrough curve (BTC), obtained from the laboratory column-or field-scale experiments, to the advection-dispersion-deposition model is used to estimate the values of deposition rate coefficients. Because of their small size (less than 100 nm), nanoparticles, a sub-class of colloids, may interact with the porous medium in a different way as compared to the larger colloids, resulting in different retention mechanisms for nanoparticles and micron-sized particles. This emphasizes the need to study nanoparticles separately from larger, micrometer-sized colloids to better understand nanoparticle retention mechanisms. The work reported in this thesis contributes towards developing mathematical models to predict nanoparticle movement in porous media. A comprehensive mechanistic approach is employed by integrating pore-scale processes into Darcy-scale models through pore-network modeling to upscale nanoparticle transport in saturated porous media to the Darcy scale, and to develop correlation equations for the Darcy-scale deposition parameters in terms of various measurable parameters at Darcy scale. Further, a one-dimensional mathematical model to simulate the co-transport of viruses and colloids in partially saturated porous media is developed to understand the relative importance of various interactions on virus transport in porous media. Pore-network modeling offers a valuable upscaling tool to express the macroscopic behavior by accounting for the relevant physics at the underlying pore scale. This is done by idealizing the pore space as an interconnected network of pore elements of different sizes and variably connected to each other, and simulating flow and transport through the network of pores, with the relevant physics implemented on a pore to pore basis (Raoof, 2011). By comparing the results of pore-network modeling with an appropriate mathematical model describing the macro-scale behavior, a relationship between the properties at the macro scale and those at the pore scale can be obtained. A three dimensional multi-directional pore-network model, PoreFlow, developed by Raoof et al. (2010, 2013) is employed in this thesis, which represents the porous medium as an interconnected network of cylindrical pore throats and spherical pore bodies, to upscale nanoparticle transport from pore scale to the Darcy scale. The first step in this procedure is to obtain relationships between adsorbed mass and aqueous mass for a single pore. A mathematical model is developed to simulate nanoparticle transport in a saturated cylindrical pore by solving the full transport equation, considering various processes such as advection, diffusion, hydrodynamic wall effects, and nanoparticle-collector surface interactions. The pore space is divided into three different regions: bulk, diffusion and potential regions, based on the dominant processes acting in each of these regions. In both bulk and diffusion regions, nanoparticle transport is governed by advection and diffusion. However, in the diffusion region, the diffusion is significantly reduced due to hydrodynamic wall effects. Nanoparticle-collector interaction forces dominate the transport in the potential region where deposition occurs. A sensitivity analysis of the model indicates that nanoparticle transport and deposition in a pore is significantly affected by various pore-scale parameters such as the nanoparticle and collector surface potentials, ionic strength of the solution, flow velocity, pore radius, and nanoparticle radius. The model is found to be more sensitive to all parameters under favorable conditions. It is found that the secondary minimum plays an important role in the deposition of small as well as large nanoparticles, and its contribution is found to increase as the favorability of the surface for adsorption decreases. Correlation equations for average deposition rate coefficients of nanoparticles in a saturated cylindrical pore under unfavorable conditions are developed as a function of nine pore-scale parameters: the pore radius, nanoparticle radius, mean flow velocity, solution ionic strength, viscosity, temperature, solution dielectric constant, and nanoparticle and collector surface potentials. Advection-diffusion equations for nanoparticle transport are prescribed for the bulk and diffusion regions, while the interaction between the diffusion and potential regions is included as a boundary condition. This interaction is modeled as a first-order reversible kinetic adsorption. The expressions for the mass transfer rate coefficients between the diffusion and the potential regions are derived in terms of the interaction energy profile between the nanoparticle and the collector. The resulting equations are solved numerically for a range of values of pore-scale parameters. The nanoparticle concentration profile obtained for the cylindrical pore is averaged over a moving averaging volume within the pore in order to get the 1-D concentration field. The latter is fitted to the 1-D advection-dispersion equation with an equilibrium or kinetic adsorption model to determine the values of the average deposition rate coefficients. Pore-scale simulations are performed for three values of Péclet number, Pe = 0.05, 5 and 50. It is found that under unfavorable conditions, the nanoparticle deposition at pore scale is best described by an equilibrium model at low Péclet numbers (Pe = 0.05), and by a kinetic model at high Péclet numbers (Pe = 50). But, at an intermediate Pe (e.g., near Pe = 5), both equilibrium and kinetic models fit the 1-D concentration field. Correlation equations for the pore-averaged nanoparticle deposition rate coefficients under unfavorable conditions are derived by performing a multiple-linear regression analysis between the estimated deposition rate coefficients for a single pore and various pore-scale parameters. The correlation equations, which follow a power law relationship with nine pore-scale parameters, are found to be consistent with the column-scale and pore-scale experimental results, and qualitatively agree with CFT. Nanoparticle transport is upscaled from pore to the Darcy scale in saturated porous media by incorporating the correlations equations for the pore-averaged deposition rate coefficients of nanoparticles in a cylindrical pore into a multi-directional pore-network model, PoreFlow (Raoof et al., 2013). Pore-network model simulations are performed for a range of parameter values, and nanoparticle BTCs are obtained from the pore-network model. Those curves are then modeled using 1-D advection-dispersion equation with a two-site first-order reversible deposition, with terms accounting for both equilibrium and kinetic sorption. Kinetic sorption is found to become important as the favorability of the surface for deposition decreases. Correlation equations for the Darcy¬scale deposition rate coefficients under unfavorable conditions are developed as a function of various measurable Darcy-scale parameters, including: porosity, mean pore throat radius, mean pore water velocity, nanoparticle radius, ionic strength, dielectric constant, viscosity, temperature, and surface potentials on the nanoparticle and grain surface. The correlation equations are found to be consistent with the observed trends from the column experiments available in the literature, and are in agreement with CFT for all parameters, except for the mean pore water velocity and nanoparticle radius. The Darcy-scale correlation equations contain multipliers whose values for a given set of experimental conditions need to be determined by comparing the values of the deposition rate coefficients predicted by the correlation equations against the estimated values of Darcy-scale deposition parameters obtained by fitting the BTCs from column or field experiments with 1-D advection-dispersion-deposition model. They account for the effect of factors not considered in this study, such as the physical and chemical heterogeneity of the grain surface and nanoparticles, flow stagnation points, grain-to-grain contacts, etc. Colloids are abundant in the subsurface and have been observed to interact with a variety of contaminants, including viruses, thereby significantly influencing their transport. A mathematical model is developed to simulate the co-transport of viruses and colloids in partially saturated porous media under steady state flow conditions. The virus attachment to the mobile and immobile colloids is described using a linear reversible kinetic model. It is assumed that colloid transport is not affected by the presence of attached viruses on its surface, and hence, colloid transport is decoupled from virus transport. The governing equations are solved numerically using an alternating three-step operator splitting approach. The model is verified by fitting three sets of experimental data published in the literature: (1) Syngouna and Chrysikopoulos (2013) and (2) Walshe et al. (2010), both on the co-transport of viruses and clay colloids under saturated conditions, and (3) Syngouna and Chrysikopoulos (2015) for the co-transport of viruses and clay colloids under unsaturated conditions. The model results are found to be in good agreement with the observed BTCs under both saturated and unsaturated conditions. Then, the developed model was used to simulate the co-transport of viruses and colloids in porous media under unsaturated conditions, with the aim of understanding the relative importance of various processes on the co-transport of viruses and colloids. The virus retention in porous media in the presence of colloids is greater under unsaturated conditions as compared to the saturated conditions due to: (1) virus attachment to the air-water interface (AWI), and (2) co-deposition of colloids with attached viruses on its surface to the AWI. A sensitivity analysis of the model to various parameters showed that virus attachment to AWI is the most sensitive parameter affecting the BTCs of both free viruses and total mobile viruses, and has a significant effect on all parts of the BTC. The free and the total mobile virus BTCs are mainly influenced by parameters describing virus attachment to the AWI, virus interactions with mobile and immobile colloids, virus attachment to solid-water interface (SWI), and colloid interactions with SWI and AWI. The virus BTC is relatively insensitive to parameters describing the maximum adsorption capacity of the AWI for colloids, inlet colloid concentration, virus detachment rate coefficient from the SWI, maximum adsorption capacity of the AWI for viruses, and inlet virus concentration.