Nanoparticle Transport In Porous Media Research Articles

Nanoparticle transport in partially saturated porous media: Attachment at fluid interfaces

Like the solid-water interface (SWI), air-water and oil-water interfaces (AWI and OWI) also act as collectors for nano-sized particles in porous media. The attachment of hydrophobic nanoparticles, which is often favorable and irreversible, is of particular interest because the transport and retention of such particles is closely linked to the fate of nanoplastics in unsaturated subsurface environments and the success of nanoremediation practices. Here, we show how a pore-network model (PNM) can be used to upscale the kinetics and extent of irreversible nanoparticle attachment at a single fluid-fluid interface under conditions of advection and dispersion in a sphere packing. By focusing on a trapped (immobile) non-wetting phase, we highlight a fundamental difference between the single-collector contact efficiency of AWI/OWI and SWI. Namely, AWI/OWI collectors, which are largely by-passed by the flowing aqueous phase, are exposed to a hydrodynamic environment dominated by diffusion. This difference has profound implications for the modelling of nanoparticle transport in porous media at the continuum (Darcy) scale. This study reveals the potential of pore network modelling as an essential complement to continuum models for upscaling the behavior of nanocolloids in porous media.

Comparison of engineered nanoparticle transport in columns of three different lengths: Transport experiments and multi-observation point modeling

This study focuses on the effectiveness of commonly-used 15 cm column lengths for investigating nanoparticle transport in porous media. Experimental tests examined the transport and retention behaviors of two types of nanoparticles, graphene oxide (GO) and titanium dioxide (TiO2) nanoparticles, in saturated sand columns of different lengths (15, 30 and 45 cm), while considering key environmental factors like ionic strength (IS, 1–50 mM), flow rate (1–3 mL min−1), and grain size (150–850 μm). In the 15 cm columns, both GO and TiO2 transport decreased with higher IS and lower flow rate; grain size affected GO and TiO2 differently. Smaller grain size increased GO retention in the sand columns through straining, thus weakening GO mobility, whereas increased fluid shear force suppressed the ripening of TiO2, enhancing its migration. Similar environmental effects were noted in longer columns (30 and 45 cm), but fitted transport parameters (Smax and k) and predicted long-term mobility (Lmax) indicated that 15 cm columns might underestimate nanoparticle mobility. Blocking and ripening models based on single and multiple observation points to simulate nanoparticle transport and retention showed that predictions aligned well with experimental data. These results indicate that using combinations of columns of different lengths to achieve multiple observation points improves model prediction accuracy; in single-column experiments, the 45 cm and 30 cm columns respectively better predict the mobility range of GO and TiO2 compared to 15 cm columns.

Transport and deposition of nanoparticles in porous media at the pore scale using an Eulerian-Lagrangian method

BackgroundNanoparticle deposition in a subsurface environment decreases the amount of material in the bulk fluid, increases surface roughness, and affects nanofluid flow. Nanoparticle transport in porous media is a complex, multiscale phenomenon. Its macroscopic behavior is determined by phenomena at the pore-scale. Hence, it is necessary and of interest to study the transport and deposition of nanoparticles in porous media using a pore-scale approach. MethodsA computational solver is developed using an Eulerian-Lagrangian approach within the OpenFOAM library. A Berea sandstone and three open-cell metal foams with porosities of 0.8, 0.85, and 0.9, constructed by Laguerre-Voronoi tessellation, were analyzed to study nanoparticle behavior in water. Significant findingsNanoparticle deposition on solid surfaces is influenced by porosity, double layer thickness (NDL), surface potential magnitude (NE1), and nanoparticle diameter. Brownian motion that has a significant impact on nanoparticle transport and deposition is also examined. For nanoparticles with 20 nm diameter, the fraction of particles deposited in foam with 0.8 porosity is about 17 % more than the foam with 0.9 porosity. This difference decreases with nanoparticle diameter. For the particles with 100 nm diameter and more, the effects of Brownian motion on nanoparticle deposition becomes negligible.

Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media

Reservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, this work creates artificial datasets using a mathematical model. It predicts nanoparticle transport behavior using machine learning techniques, including gradient boosting regression, decision trees, random forests, and artificial neural networks. Utilizing the scikit-learn toolkit, strategies for data preprocessing, correlation, and feature importance are addressed. Furthermore, the GridSearchCV algorithm is used to optimize hyperparameter tuning. The mean absolute error, R-squared correlation, mean squared error, and root means square error are used to assess the models. The ANN model has the best performance in forecasting the transport of nanoparticles in porous media, according to the results.

Time resolved pore scale monitoring of nanoparticle transport in porous media using synchrotron X-ray μ-CT

Capturing nanoparticle retention in 4D via ex situ μ-CT.

Transport and Retention of Poly(Acrylic Acid-co-Maleic Acid) Coated Magnetite Nanoparticles in Porous Media: Effect of Input Concentration, Ionic Strength and Grain Size.

Understanding the physicochemical factors affecting nanoparticle transport in porous media is critical for their environmental application. Water-saturated column experiments were conducted to investigate the effects of input concentration (Co), ionic strength (IS), and sand grain size on the transport of poly(acrylic acid-co-maleic acid) coated magnetite nanoparticles (PAM@MNP). Mass recoveries in the column effluent ranged from 45.2 to 99.3%. The highest relative retention of PAM@MNP was observed for the lowest Co. Smaller Co also resulted in higher relative retention (39.8%) when IS increased to 10 mM. However, relative retention became much less sensitive to solution IS as Co increased. The high mobility is attributed to the PAM coating provoking steric stability of PAM@MNP against homoaggregation. PAM@MNP retention was about 10-fold higher for smaller grain sizes, i.e., 240 µm and 350 µm versus 607 µm. The simulated maximum retained concentration on the solid phase (Smax) and retention rate coefficient (k1) increased with decreasing Co and grain sizes, reflecting higher retention rates at these parameters. The study revealed under various IS for the first time the high mobility premise of polymer-coated magnetite nanoparticles at realistic (<10 mg L−1) environmental concentrations, thereby highlighting an untapped potential for novel environmental PAM@MNP application usage.

Enhanced oil recovery by nanoparticles flooding: From numerical modeling improvement to machine learning prediction

Nowadays, enhanced oil recovery using nanoparticles is considered an innovative approach to increase oil production. This paper focuses on predicting nanoparticles transport in porous media using machine learning techniques including random forest, gradient boosting regression, decision tree, and artificial neural networks. Due to the lack of data on nanoparticles transport in porous media, this work generates artificial datasets using a numerical model that are validated against experimental data from the literature. Six experiments with different nanoparticles types with various physical features are selected to validate the numerical model. Therefore, the researchers produce six datasets from the experiments and create an additional dataset by combining all other datasets. Also, data preprocessing, correlation, and features importance methods are investigated using the Scikit-learn library. Moreover, hyperparameters tuning are optimized using the GridSearchCV algorithm. The performance of predictive models is evaluated using the mean absolute error, the R-squared correlation, the mean squared error, and the root mean squared error. The results show that the decision tree model has the best performance and highest accuracy in one of the datasets. On the other hand, the random forest model has the lowest root mean squared error and highest R-squared values in the rest of the datasets, including the combined dataset. Cited as: Alwated, B., El-Amin, M.F. Enhanced oil recovery by nanoparticles flooding: From numerical modeling improvement to machine learning prediction. Advances in Geo-Energy Research, 2021, 5(3): 297-317, doi: 10.46690/ager.2021.03.06

Simulation Study of Nanoparticle Transport in Porous Media: Effects of Salinity and Reservoir Parameters

Although experimental studies confirmed the effectiveness of nanoparticles in enhanced oil recovery applications, no comprehensive investigation has been carried out to reveal the effect of different subsurface factors on this improvement. Proper application of nanoparticles mainly depends on their ability to travel long distances within a reservoir without agglomeration, retention and blocking the pore throats. This study strengthens our understanding about the effect of the main subsurface factors on the nanofluid-assisted enhanced oil recovery. For doing so, a transport approach utilizing kinetic Langmuir model is developed and validated using experimental data. Thereafter, the effect of reservoir rock type and its properties (clay content and grain size), salinity of injected fluid, and reservoir temperature on the transport and retention of nanoparticles in porous media in relation to enhanced oil recovery methods is investigated. Since the amount of nanoparticles in the injected fluid and on the rock surface (as deposited) control the mobility and wettability alteration, the effect of subsurface factors and salinity of injected fluid on this deposition is also analyzed. The results showed that the rock type and its properties significantly affect the transport and retention of nanoparticles in porous media. It was also found that the brine salinity has the greatest impact on the amount of nanoparticles deposited on the rock surface. The surface covered by NPs increased from 10 to 82 % after changing salinity from 3 weight percent NaCl to API brine.

Effect of deposition, detachment and aggregation processes on nanoparticle transport in porous media using Monte Carlo simulations

A novel off-lattice three-dimensional coarse-grained Monte Carlo model is developed to study engineered nanoparticle (ENP) behavior in porous media.

Nanoparticle Tracking to Probe Transport in Porous Media.

From the granular and fractured subsurface environment to highly engineered polymer membranes used in pharmaceutical purification, porous materials are ubiquitous in nature and industrial applications. In particular, porous media are used extensively in processes including water treatment, pharmaceutical sterilization, food/beverage processing, and heterogeneous catalysis, where hindered mass transport is either essential to the process or a necessary but undesirable limitation. Unfortunately, there are currently no universal models capable of predicting mass transport based on a description of the porous material because real porous materials are complex and because many coupled dynamic mechanisms (e.g., adsorption, steric effects, hydrodynamic effects, electrostatic interactions, etc.) give rise to the observed macroscopic transport phenomena.While classical techniques, like nuclear magnetic resonance and dynamic light scattering, provide useful information about mass transport in porous media at the ensemble level, they provide limited insight into the microscopic mechanisms that give rise to complex phenomena such as anomalous diffusion, hindered pore-space accessibility, and unexpected retention under flow, among many others. To address this issue, we have developed refractive index matching imaging systems, combined with single-particle tracking methods, allowing the direct visualization of single-particle motion within a variety of porous materials.In this Account, we summarize our recent efforts to advance the understanding of nanoparticle transport in porous media using single-particle tracking methods in both fundamental and applied scenarios. First, we describe the basic principles for two-dimensional and three-dimensional single-particle tracking in porous materials. Then, we provide concrete examples of nanoparticle transport in porous materials from two perspectives: (1) understanding fundamental elementary particle transport processes in porous media, including pore accessibility and cavity escape, which limit transport in porous media, and (2) facilitating applications in industrial processes, e.g., by understanding the mechanisms of particle fouling and remobilization in filtration membranes. Finally, we provide an outlook of opportunities associated with investigating other types of mass transport in confined environments using single-particle tracking methods, including electrophoretic and self-propelled motion.

ICCM2016: Implementation of the Parareal Algorithm to Optimize Nanoparticle Transport in Porous Media Simulations

This research study aims to conduct the analysis and implementation of efficient algorithms for simulations of micro and nanoparticle transport models in porous media, coupled with the Darcy–Forchheimer fluid model, modified to include electromagnetic effects. The schemes developed were implemented via a parallel infrastructure for benchmark problems with a flexible algorithm that is efficient, robust, and stable. These improvements in the reliability and efficiency of simulations of nanoparticle transport in porous media contribute to the creation of an efficient method to counteract the contaminants in groundwater, and ultimately increase the availability of clean drinking water.

Modeling Nanoparticle Transport in Porous Media in the Presence of a Foam

Nano-remediation is a promising in situ remediation technology. It consists in injecting reactive nanoparticles (NPs) into the subsurface for the displacement or the degradation of contaminants. However, due to the poor mobility control of the reactive nanoparticle suspension, the application of nano-remediation has some major challenges, such as high mobility of the particles, which may favor override of the contamination, and particle aggregation, which can lead to a limited distance of influence. Previous experimental studies show the potential of combining nano-remediation with foam flooding to overcome these issues. However, in order to design and optimize the process, a model which couples nanoparticle and foam transport is necessary. In this paper, a mechanistic model to describe the transport of NPs with and by a foam is presented. The model considers the delivery of nanoscale zero-valent iron (nZVI) and accounts for the processes of aggregation, attachment/detachment, and generation/destruction. Simulations show that when NPs are dispersed in the liquid phase, even in the presence of a foam, they may travel much slower than the NPs carried by the foam bubbles. This is because the nanoparticles in suspension are affected by the attachment onto the rock walls and straining at the pore throats. When the nanoparticle surface is, instead, modified in order to favor their adsorption onto the gas bubbles, NPs are carried by the foam without retardation, except for the small fraction suspended in the liquid phase. Moreover, very stable high-quality foam ($$f_\mathrm{g}$$), i.e., 80–90 vol% of gas, can be attained using properly surface-modified nZVI (i.e., a nanoparticle-stabilized foam), allowing a significant reduction of water for the operation, while increasing the efficiency of nZVI delivery, even in a low-permeability medium within the shallow subsurface.

Critical Review of Stabilized Nanoparticle Transport in Porous Media

Over the past few decades, due to the special features (i.e., easily produced, large-surface-area-to-volume ratio, and engineered particles with designed surface properties), nanoparticles have not only attracted great attentions from the oil and gas industry but also had various applications from drilling and completion, reservoir characterization, to enhanced oil recovery (EOR). As sensors or EOR agents, thus, fate and behavior of nanoparticles in porous media are essential and need to be investigated thoroughly. Nevertheless, most of the published review papers focus on particle transport in saturated porous media, and all of them are about steady-state flow conditions. So far, no attempts have been extended to systematically review current knowledge about nanoparticle transport in porous media with single-phase and two-phase flow systems under both steady-state and unsteady-state conditions. Accordingly, this review will discuss nanoparticle transport phenomena in porous media with its focus on the filtration mechanisms, the underlying interaction forces, and factors dominating nanoparticle transport behavior in porous media. Finally, mathematical models used to describe nanoparticle transport in porous media for both single-phase flow and two-phase flow under steady-state and transient flow conditions will be summarized, respectively.

Magnetic susceptibility monitoring and modelling (MSMM): A non-invasive method for acquiring and modelling exceptionally large datasets from column experiments with manufactured nanoparticles

Identifying and quantifying the processes governing nanoparticle transport in porous media using breakthrough curves with or without retention profiles from laboratory column experiments is frequently subject to uncertainty due to the limited information content of such datasets. An integrated system of automated, non-invasive magnetic susceptibility monitoring and numerical modelling (MSMM) has been developed to provide exceptionally detailed datasets for assessing the validity of transport models of magnetic nanoparticles within a column. MSMM produces the equivalent of a breakthrough curve for each monitored location along the column and uses the enhanced dataset to constrain numerical models more effectively. The results of 2 example column experiments using magnetite nanoparticles are presented to demonstrate the approach: (i) using quartz sand (with 46,002 susceptibility measurements over 37 h) and (ii) using crushed Triassic Sandstone (with 19,654 measurements over 20 h). The quartz sand experiment showed no nanoparticle retention: MSMM showed the system could be well described by an advection-dispersion model, which predicted a breakthrough curve consistent with that derived from magnetic monitoring and with the breakthrough curve acquired independently using a fluorescein tracer. In contrast, no breakthrough was observed in the sandstone experiment, but even in the absence of a breakthrough curve, MSMM indicated that the retention processes were spatially heterogeneous and consistent with a combination of parameterised models of physical straining and limited capacity irreversible attachment.

Determination of Nanoparticle Macrotransport Coefficients from Pore Scale Processes

Nanoparticle transport in porous media is modeled using a hierarchical set of differential equations corresponding to pore scale and macroscale. At the pore scale, movement and interaction of a single particle with a solid matrix is modeled using the advection–dispersion–sorption equation. A single nanoparticle entering the space encounters viscous, diffusion and surface forces. Surface forces (electrostatic and van der Waals forces) between nanoparticles and mineral grains appear as sorption propensity on solid matrix boundary condition. These local events are then transformed into a macroscale continuum by imposing periodic boundary conditions for contiguous unit cells representing porous media and using a scheme of moment analysis. At the macroscale, propagation and retention of particles are characterized by three position-independent coefficients: mean nanoparticle velocity vector $${\bar{\mathbf{U}}}^*$$ , macroscopic dispersion coefficient $${\bar{\mathbf{D}}}^*$$ , and mean nanoparticle retention rate constant $${\bar{K}}^*$$ . The modeling results are validated with a set of nanoparticle transport tests in porous microchips. We also present simulations of realistic porous media, where an actual image of sandstone samples is processed into binary tones. The representative unit cells are constructed from the resulting binary image by searching for areas within the sample with maximum similarities to the whole sample in terms of porosity and specific surface area, which are found to show strong correlations with the resulting $${\bar{\mathbf{U}}}^*$$ and $${\bar{K}}^*$$ , respectively.

Mechanistic study of nanoparticles deposition and release in porous media

Physicochemical interaction between the nanoparticles and the pore walls can cause significant retention of nanoparticles in porous media. The objective here is to provide mechanistic model based on Extended DLVO theory to study the rate of deposition and release of nanoparticles in porous media at different temperature, ionic strength, and pH. Empirical equation has been derived to calculate zeta potential at different temperature, ionic strength, and pH. The interaction energy can be with/without energy barrier between the nanoparticles and the pore surface. The rate of deposition and release of nanoparticles in each case has been derived. Numerical model has been used to compare the theoretically calculated rates with several experimental data. Increasing the temperature decreases the energy barrier height and increases the rate of deposition. With increasing the ionic strength, the thickness of the electrostatic double layer decreases and hence the rate of deposition increases. The effect of pH on the rate of deposition depends on the location of environment pH with respect to the isoelectric point of the nanoparticles and rock. For the extreme values of pH, energy barrier exists and rate of deposition is low. However, when the pH of the solution is between the isoelectric points of the nanoparticles and rock, the energy barrier decreases and the rate of deposition increases. The rate of deposition is time dependent as it decreases with increasing the covered rock surface. The effect of surface roughness has been included in the model using the effective height and density of the surface roughness distribution. Finally, these theoretically calculated rate values are used in a numerical model of the advection-dispersion equation with source/sink term. Several experimental results have been perfectly matched that validate the theoretical calculations of the rate of deposition. The new mechanistic model for nanoparticles can be used to determine the fate of nanoparticles in porous media under different conditions of temperature, ionic strength, concentration, and pH. This model can help to understand the nanoparticles transport in porous media and effectively design nanoparticles fluid for injection into oil and gas reservoirs.

Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network

AbstractThe continuing rapid expansion of industrial and consumer processes based on nanoparticles (NP) necessitates a robust model for delineating their fate and transport in groundwater. An ability to reliably specify the full parameter set for prediction of NP transport using continuum models is crucial. In this paper we report the reanalysis of a data set of 493 published column experiment outcomes together with their continuum modeling results. Experimental properties were parameterized into 20 factors which are commonly available. They were then used to predict five key continuum model parameters as well as the effluent concentration via artificial neural network (ANN)‐based correlations. The Partial Derivatives (PaD) technique and Monte Carlo method were used for the analysis of sensitivities and model‐produced uncertainties, respectively. The outcomes shed light on several controversial relationships between the parameters, e.g., it was revealed that the trend of with average pore water velocity was positive. The resulting correlations, despite being developed based on a “black‐box” technique (ANN), were able to explain the effects of theoretical parameters such as critical deposition concentration (CDC), even though these parameters were not explicitly considered in the model. Porous media heterogeneity was considered as a parameter for the first time and showed sensitivities higher than those of dispersivity. The model performance was validated well against subsets of the experimental data and was compared with current models. The robustness of the correlation matrices was not completely satisfactory, since they failed to predict the experimental breakthrough curves (BTCs) at extreme values of ionic strengths.

Upscaling of nanoparticle transport in porous media under unfavorable conditions: Pore scale to Darcy scale

Transport and deposition of nanoparticles in porous media is a multi-scale problem governed by several pore-scale processes, and hence, it is critical to link the processes at pore scale to the Darcy-scale behavior. In this study, using pore network modeling, we develop correlation equations for deposition rate coefficients for nanoparticle transport under unfavorable conditions at the Darcy scale based on pore-scale mechanisms. The upscaling tool is a multi-directional pore-network model consisting of an interconnected network of pores with variable connectivities. Correlation equations describing the pore-averaged deposition rate coefficients under unfavorable conditions in a cylindrical pore, developed in our earlier studies, are employed for each pore element. Pore-network simulations are performed for a wide range of parameter values to obtain the breakthrough curves of nanoparticle concentration. The latter is fitted with macroscopic 1-D advection-dispersion equation with a two-site linear reversible deposition accounting for both equilibrium and kinetic sorption. This leads to the estimation of three Darcy-scale deposition coefficients: distribution coefficient, kinetic rate constant, and the fraction of equilibrium sites. The correlation equations for the Darcy-scale deposition coefficients, under unfavorable conditions, are provided as a function of 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 of the particle and grain surfaces. The correlation equations are found to be consistent with the available experimental results, and in qualitative agreement with Colloid Filtration Theory for all parameters, except for the mean pore water velocity and nanoparticle radius.

Mathematical modeling and simulation of nanoparticles transport in heterogeneous porous media

Nanoparticle applications in the petroleum industry have grown recently especially in EOR and waterflooding. Although the nanoparticles are small, they can be retained in the porous media by three different damage mechanisms i.e. surface deposition, mono-particle plugging, and multi-particles plugging. This could severely decrease the porosity and permeability of the porous medium. Consequently, a numerical model that accurately describes these damage mechanisms is essential for forecasting and optimization of nanoparticles transport in porous media. In this paper, we have developed a mathematical model that combines Darcy and convection-diffusion equation to describe fluid flow, nanoparticles transport, and interaction in porous media. Pore throat size distribution is used to characterize the heterogeneity. Permeability field is generated as a function of the pore throat size distribution. Pore throat size and permeability distributions are dynamic functions of the nanoparticles deposition and plugging. The mathematical model is solved on a two-dimensional domain using alternating direction implicit scheme. The model is validated with experimental data to obtain the model parameters. Sensitivity analysis is presented using the proposed numerical model. The model shows that each of the three damage mechanisms could be dominant at specific conditions. Dimensional analysis is then used to derive a correlation that relates the degree of damage to main dimensionless numbers that control the efficiency of nanoparticle transport. The preliminary numerical results demonstrate that nanoparticle size, concentration, injection rate and permeability are the dominant factors that control the degree of formation damage.

Quantified Pore-Scale Nanoparticle Transport in Porous Media and the Implications for Colloid Filtration Theory.

This study evaluates the pore-scale distribution of silver nanoparticles during transport through a sandy porous medium via quantitative synchrotron X-ray computed microtomography (qSXCMT). The associated distributions of nanoparticle flow velocities and mass flow rates were obtained by coupling these images with computational fluid dynamic (CFD) simulations. This allowed, for the first time, the comparison of nanoparticle mass flow with that assumed by the standard colloid filtration theory (CFT) modeling approach. It was found that (i) 25% of the pore space was further from the grain than assumed by the CFT model; (ii) the average pore velocity agreed well between results of the coupled qSXCMT/CFD approach and the CFT model within the model fluid envelope, although the former were 2 times larger than the latter in the centers of the larger pores and individual velocities were upwards of 20 times those in the CFT model at identical distances from grain surfaces ; and (iii) approximately 30% of all nanoparticle mass and 38% of all nanoparticle mass flow occurred further away from the grain surface than expected by the CFT model. This work suggests that a significantly smaller fraction of nanoparticles than expected will contact a grain surface by diffusion via CFT models, likely contributing to inadequate CFT model nanoparticle transport predictions.