Subsurface Applications Research Articles

Diffusion of CO2 in single-step silica nanofluid for subsurface utilization: an experimental study.

This study investigates the role of single-step silica nanofluids as additives to increase CO2 absorption in polymeric solutions for proposed oilfield applications. Using pressure decay approach, the study investigates the applicability of single-step silica nanofluids for CO2 absorption in a high pressure-high temperature (HPHT) cell. Various parameters like nanoparticle size (30-120nm) and concentration (0.1-1 wt%) were investigated to ascertain the absorption performance of the nanofluids and optimization their application in subsurface applications as carrier fluids for CO2. The solutions under observation (deionized water and silica nanofluids) were pressurized under the desired pressure and temperature inside a stirring pot and the decline in pressure was continuously noted. To comprehensively cover the near-reservoir field conditions, the CO2 absorption was investigated in the pressure range of 5-10MPa and at temperatures of 30-90°C. While increasing the nanoparticle concentration (from 0.1 to 1 wt%) increased the CO2 absorption (evident by the sharper decline in pressure), increasing the nanoparticle size reduced the absorption capacity of the nanofluids as a lesser volume of decline in pressure was noted. Furthermore, increasing the temperature of the experimental investigation caused a major reduction (12-19%) in the pressure decay. However, it was also observed that higher pressure (> 7.5MPa) was detrimental for CO2 absorption (due to its supercritical nature). Adding salt (sodium chloride, NaCl) was found to massively lower (up to 33%) while adding surfactant (sodium dodecyl sulfate, SDS) slightly increased the amount of CO2 absorption (in presence of salinity). Based on the observations of this study, the use of single-step silica nanofluids as CO2 carrier fluids is recommended for oilfield conditions where salinity is less than 4 wt%.

Machine learning for rock mechanics problems; an insight

Due to inherent heterogeneity of geomaterials, rock mechanics involved with extensive lab experiments and empirical correlations that often lack enough accuracy needed for many engineering problems. Machine learning has several characters that makes it an attractive choice to reduce number of required experiments or develop more effective correlations. The timeliness of this effort is supported by several recent technological advances. Machine learning, data analytics, and data management have expanded rapidly in many commercial sectors, providing an array of resources that can be leveraged for subsurface applications. In the last 15 years, deep learning in the form of deep neural networks, has been used very effectively in diverse applications, such as computer vision, seismic inversion, and natural language processing. Despite the remarkable success in these and related areas, deep learning has not yet been widely used in the field of scientific computing specially when it comes to subsurface applications due to the lack of large amount of data to train algorithms. In this paper, we review such efforts and try to envision future game-changing advances that may impact this field.

Hysteresis in permeability evolution simulated for a sandstone by mineral precipitation and dissolution

Abstract. Mineral dissolution and precipitation can substantially affect rock permeability, which is a critical parameter for a broad range of geological subsurface applications. Virtual experiments on digital pore-scale samples represent a powerful and flexible approach to understand the impact of microstructural alterations on evolving hydraulic rock behaviour and quantify trends in permeability. In the present study, porosity-permeability relations are simulated for a precipitation-dissolution cycle within a typical reservoir sandstone. A hysteresis in permeability is observed depending on the geochemical process and dominating reaction regime, whereby permeability of the six investigated reaction paths varies by more than two orders of magnitude at a porosity of 17 %. Controlling parameters for this hysteresis phenomenon are the closure and re-opening of micro-scale flow channels, derived from changes in pore throat diameter and connectivity of the pore network. In general, a transport-limited regime exhibits a stronger impact on permeability than a reaction-limited regime, which uniformly alters the pore space. In case of mineral precipitation, higher permeability reduction results from successive clogging of pore throats, whereas in case of dissolution, permeability significantly increases due to a widening of existing flow paths. Both, the geochemical process and dominating reaction regime govern characteristic microstructural alterations, which cannot be simply reversed by the inversion of the geochemical processes itself. Hence, permeability evolution clearly depends on the hydrogeochemical history of the sample.

Hydrodynamics of gas-liquid displacement in porous media: Fingering pattern evolution at the breakthrough moment and the stable state

Gas-liquid displacement in porous media widely exists in many terrestrial/extraterrestrial subsurface resource extraction and utilization applications. The typical fingering displacement during gas invading has been well identified through extensive research efforts. Yet, the evolution of fingering structures after invading breakthrough is rarely reported. Herein, through a joint approach of experimental flow imaging and digital image processing, we investigated the gas-liquid fingering displacement in a porous-patterned microfluidic chip from the breakthrough moment until reaching the steady state. With a wide range of capillary number Ca and viscosity ratio M, we visualized the evolution of finger morphologies in different flow regimes including capillary fingering (CF), viscous fingering (VF), and crossover zone (CZ). Interestingly, we found that finger structures of CF regime remain the same after the breakthrough, whereas fingers of VF regime keep expanding until almost all the pore space is invaded and eventually reaches to steady state. Followed with experimental observations, a comparative quantification of fingering patterns was also conducted in terms of invasion velocity, phase saturation and fractal dimension. A dramatic increase of gas saturation, from 0.15 to 0.60 at the case of Log10Ca=-5.17 and Log10M=-2.78, is obtained in the VF regime when the steady state is reached, so is the fractal dimension (from 0.14 to 0.16, even higher than one of CF). The underlying mechanism of such fingering expansion in VF is further revealed from the time evolution of fingering after breakthrough. A previously unobserved fingering cycle, consisting of new finger forming, cap invading, breakthrough and finger vanishing, keeps repeating until the saturation reaches the maximum. We believe that these findings are of significance in evaluating extraction effectiveness, economic benefits and storage safety for subsurface applications.

Reactive Transport Modeling with a Coupled OpenFOAM®-PHREEQC Platform

Reactive transport modeling has established itself as a key player to analyze sophisticated hydro-geochemical interactions that occur over spatiotemporal scales on par with subsurface applications. In this paper, we benchmarked a new reactive transport package—porousMedia4Foam at the continuum scale considering complex and well-established cases available in the reactive transport modeling community. porousMedia4Foam was born by the successful coupling of two open-source packages—OpenFOAM® and PHREEQC. The flow governing equations, transport of species and the evolution of porous media properties are handled by OpenFOAM®, whereas the geochemistry is exclusively handled by PHREEQC. We further demonstrated the capability of using porousMedia4Foam to investigate reactive transport processes considering unstructured meshes. As porousMedia4Foam is an open-source package with included benefits to account for reactive transport processes occurring at various scales—pore, hybrid, and Darcy scales, we believe that porousMedia4Foam opens a new dimension to analyze reactive transport physics for various intriguing subsurface applications.

A thermodynamically consistent [formula omitted]-integral formulation for fluid-driven fracture propagation in poroelastic continua

We present a model for fluid-driven propagation of discrete fractures in poroelastic continua. The developed model incorporates skin (surface permeability) and fluid slip effects at the fracture walls, which are especially relevant for subsurface applications. The development of a fracture propagation criterion requires adequate incorporation of all dissipative mechanisms involved. This work proposes a thermodynamically consistent J-integral formulation for fracture propagation based on the theory of linear elastic fracture mechanics. The proposed poroelastic J-integral formulation is validated in the stationary setting using analytical approximations. The propagation criterion is implemented in an extended finite element method (X-FEM) simulation framework for propagating fractures. This simulation framework is used to gain insight into the role of skin and slip effects on fluid-driven fracture propagation in poroelastic media.

Upscaling Hydrodynamic Dispersion in Non‐Newtonian Fluid Flow Through Porous Media

AbstractHydrodynamic dispersion in flow through porous media is an essential phenomenon in many geosystems. While dispersion in flow of Newtonian fluids is relatively well‐understood, many subsurface applications, such as groundwater remediation, use the flooding system with non‐Newtonian fluids, such as polymer oxidants. Despite its significance, however, very limited studies have been carried out focusing on hydrodynamic dispersion in flow of non‐Newtonian fluids. The present study addresses a fundamental question regarding how solute transport in flow of non‐Newtonian fluids in porous media differs from the Newtonian limit. We report on the results of an extensive pore‐scale study of the upscaling of advection‐diffusion in flow of a non‐Newtonian, shear thinning fluid through disordered and spatially‐correlated porous media, using an advanced GPU‐based pore‐scale simulator in order to delineate the effects of the fluid's rheology, dynamics of fluid flow, and pore‐scale spatial correlations on hydrodynamic dispersion. The simulations indicate a surprising non‐monotonic relation between the injection rate (or injection velocity) and the dispersivity. While dispersivity in Newtonian fluid flow in porous media is constant in the flow regime that we study, we find, however, that the dispersivity in flow of non‐Newtonian fluids is shear‐dependent. This highlights the gap in the existing theories of transport through porous materials for non‐Newtonian fluids, which can lead to erroneous estimates of dispersion coefficients in porous materials when the fluid's rheology cannot be represented by Newtonian mechanics.

A Decision-Support System for Economic Feasibility of Subsurfaced Poultry Litter

Cattle (Bos spp.) producers routinely spread fertilizer or broadcast poultry (Gallus gallus L.) litter to improve forage production. With poultry litter widely available across the southeastern United States and being a cheaper source of plant nutrition than commercial fertilizer when proximal to application sites, the novel application of litter below the soil surface, while costly, allows for greater nutrient retention than broadcasting. Since quantifying costs and benefits of subsurfaced litter is complex, we develop and present a decision support spreadsheet for automated farm-scale model comparisons across modes of nutrient management. We compare subsurfaced litter to commercial fertilizer and broadcast litter in user-specific scenarios involving equipment choices (new, used, custom), desired nutrient needs for crops grown, litter nutrient concentration, and interval between litter application given the slow-release nature of litter compared to commercial fertilizer. Using Visual Basic for Applications programming available with Microsoft Excel®, we develop a decision aid that collects relevant user-specific farm parameters that allow customization of output. Performance data of the innovative subsurface application equipment as observed during field operation and published engineering equipment performance standards, combined with cost information, allow estimation of relative performance metrics. Break-even and sensitivity analyses are performed to identify key equipment modifications or subsidies needed to speed adoption of subsurfacing poultry litter to minimize odor and nutrient runoff externalities experienced with broadcast litter. Results showed that subsurfaced litter can compete with commercial fertilizer on a cost/return basis and especially so in situations where organic farming practices may be targeted. With a longer interval between litter application, enhanced annual capacity utilization of the applicator, and a good match between nutrient needs of crops fertilized and litter nutrient content, the decision aid demonstrated cost savings with subsurfaced litter over commercial fertilizer while being more or less cost-neutral in comparison to broadcast litter. The decision aid lends itself to estimating operation-specific subsidies needed to encourage adoption of subsurface application of litter with the intent to reduce nutrient runoff and odor externalities. Alternatively, the sensitivity analyses pinpointed the need to enhance the performance rate of subsurface application that may be achievable by increasing the implement’s width.

Impact of fracture orientation on supercritical carbon dioxide -foam performance and optimization in sandstones

Natural or induced fractures pose a significant challenge for subsurface energy extraction, carbon storage, and aquifer contaminant remediation. They provide high permeability channels that disrupt the natural state of the flow. Fractures prevent pressure buildup and induce early breakthrough of displacing fluid, leaving most of the target fluids behind. Foams can alleviate this problem by providing fluid diversion. The primary function of fluid diversion is to reduce the mobility inside the higher permeable regions by increasing the viscosity, resulting in high pressure buildup and allowing the displacing fluid to be diverted to the low permeable regions of the matrix. However, foam optimization must be performed prior to field application to ensure the foam's stability and compatibility with in-situ fluids. Moreover, the nature of the reservoir rock plays a vital role in maintaining the integrity of the foam system required for subsurface applications. This is particularly important in the case of fractured porous media as fracture properties, such as roughness and orientation, could significantly impact the in-situ foam generation/destruction processes. This study systematically investigates the effect of fractures and their orientations on scCO2 foam performance in a homogeneous sandstone. Furthermore, the foam system was optimized in both unfractured and fractured sandstone rocks at reservoir conditions relevant to a target field for dual-purpose CO2 storage-oil recovery. The surfactant solution used for foam generation was developed using the zwitterionic surfactant Lauramine Oxide where the optimum composition was found to be 0.6 wt% surfactant and 15 wt% total salts with an 80:20 ratio NaCl:CaCl2. Results also confirmed a shear thinning behavior which is typical of a zwitterionic-based foam. Moreover, the 10° fractured core exhibited a 50% higher apparent viscosity than the unfractured one which proves the foam's great ability in fluid diversion.

A biotic perspective on the Oligo-Miocene evolution of the Maldives carbonate platform from forward stratigraphic modelling (Indian Ocean)

Forward stratigraphic modelling is a fast-developing modelling approach, used to test conceptual models, and predict stratigraphic architecture and depositional facies from basin to reservoir scales. Published subsurface applications demonstrate its added value by integrating multidisciplinary data as well as geological concepts into its constraints. When applied to carbonate depositional systems, composed of multiple sediment factories, the co-operating and interdependent production mechanisms remain poorly studied. By applying the technique to a well-studied section of the Maldives carbonate platform, a specific model design—adapted to the geological age and setting, and constrained by available data—sheds light on the interaction of its carbonate producers. The results yield a naturalistic depositional facies distribution and offer insight in the changing relationship between biotic communities during the platform evolution. After calibration, the reference model unequivocally links the formerly proposed genetic model to the seismostratigraphic architecture. Furthermore, the results show how environmental changes (seemingly of secondary impact compared to changes in physical accommodation in the stratigraphic record) can induce substantial fluctuations in carbonate production rates of biotic communities, affect the ecological accommodation, and thus impact the platform architecture. Therefore, it is crucial to treat carbonate production rates during periods of environmental change as variables with associated uncertainties in a forward stratigraphic model setup.

Maintaining higher grain production with less reactive nitrogen losses in China: A meta-analysis study

Managing reactive nitrogen (Nr) in agricultural production is crucial for addressing the triple challenges of food security, climate change and environmental degradation. Intensive work has been conducted to investigate the effects of mitigation strategies on reducing Nr losses by ammonia emission (Nr-NH3), nitrous oxide emission (Nr-N2O) and nitrate leaching (Nr-NO3-) separately. This meta-analysis evaluated the efficiency of each strategy in mitigating Nr losses coupled with grain yield responses. The results indicate that producing one Megagram (Mg) of wheat grains caused higher Nr losses, twice that of rice and 17% that of maize. The Nr-NH3 and Nr-NO3- were the dominant sources of Nr losses of the three crops (96%), while Nr-NH3 only presented 86% of the total Nr losses for rice. Reducing the N rate strategy decreased the yield by 33% and the Nr losses by 62% compared with the conventional rate (150–250 kg N ha−1) as an average of the three crops. In contrast, increasing the N rate higher than 250 kg N ha−1 amplified the yield by 15% but also caused a 71% increase in Nr losses compared with the conventional rate. Although subsurface application decreased Nr losses by 5%, this study rejected this approach as an effective strategy due to a 4% yield decline on average of the grain crops. Slow-release fertilizers decreased Nr-NH3 and Nr-N2O losses by 41–58% and 54–89%, respectively, of the highest losses under urea in the three crops, but also led to yield reductions. Organic amendments achieved the highest drop in Nr-NO3- loss by 66% in maize coupled with yield declines. Biochar increased wheat and maize yields by 0.3 and 0.1 Mg, respectively, coupled with 1 kg reduction in Nr losses. On average, inhibitors augmented the grain yields by 0.2 Mg ha−1 for each 1 kg decline in Nr losses. In conclusion, for sustainable agricultural intensification, biochar (for wheat only) and inhibitors (for the three crops) are strongly recommended as mitigation strategies for Nr losses from grain crop production systems in China.

Digital transformation in rock physics: Deep learning and data fusion

Rock physics plays an essential role in geophysical reservoir characterization. It aims to build a bridge between geophysical measurements and in-situ rock and fluid properties. With the advancement of microscopic imaging and computer science, rock physics is transitioning to the digital age. This is referred to as digital rock physics (DRP). DRP provides a nondestructive and efficient way to determine physical rock properties directly from digital images. Over the last decades, it has become a routine tool in reservoir characterization by complementing or replacing expensive and time-consuming laboratory measurements. With the emergence of deep learning, DRP has advanced significantly from image processing to physical simulation. This paper presents an application of deep learning in multiscale fusion of digital rock images. It aims to overcome the trade-off between image resolution and field of view (FoV) by integrating imaging data from multiple sources including (1) 3D microcomputed tomography images at micronscale with a large FoV and (2) 2D scanning electron microscopy images at nanoscale with a small FoV. The reconstructed image integrates information of microstructures at different scales and helps characterize heterogeneous porous rocks more accurately. It is helpful to improve the prediction accuracy of effective rock properties and to have deeper insight into physical processes at pore scale. Data fusion based on deep learning would unlock new pathways for geophysical characterization of porous rocks, with broad implications for various subsurface applications such as groundwater transport, enhanced oil recovery, and geologic carbon sequestration.

Coupling the environmental impacts of reactive nitrogen losses and yield responses of staple crops in China

Cropland reactive nitrogen losses (Nr) are of the greatest challenges facing sustainable agricultural intensification to meet the increases in food demand. The environmental impacts of Nr losses and their yield responses to the mitigation strategies were not completely evaluated. We assessed the environmental impacts of Nr losses in China and coupled the efficiency of mitigation actions with yield responses. Datasets about Nr losses in China were collected, converted into potentials of acidification (AP), global warming (GWP), and aquatic eutrophication (AEP), and analyzed by a meta-analysis program. Results showed that producing 1 Mg of rice grains had the highest AP (153 kg acid equiv.), while wheat had the highest GWP and AEP (74 kg CO2 equiv. and 0.37 kg PO4 equiv., respectively). Using the conventional rates (averagely, 200, 230, and 215 kg N ha−1) of urea as a surface application to produce 131.4, 257.2, and 212.1 Tg of wheat, maize, and rice resulted in 17–33 Tg, 7–10 Tg, and 6–87 Gg of AP, GWP, and AEP, respectively. For their balanced effect on reducing AP, GWP, and AEP while maximizing yields, inhibitors, and subsurface application could be set as the best mitigation strategies in wheat production. Inhibitors usage and biochar are strongly recommended strategies for sustainable production of maize. None of the investigated strategies had a balanced effect on rice yield and the environment, thus new mitigation technologies should be developed.

How does phase behavior of surfactant/fluid/fluid systems affect fluid-fluid displacement in porous media?

Surfactant flooding is widely used in subsurface applications where high displacement efficiency is needed, such as nonaqueous phase liquid (NAPL) remediation and enhanced oil recovery (EOR). For surfactant/water/oil systems, the partitioning of surfactants in each phase is controlled by external factors, such as salinity. The third phase, microemulsions, which locates between water and oil, can be formed spontaneously at certain salinities. Therefore, the transfer of surfactants from water to other phases, which depends on salinity, can occur during surfactant flooding, resulting in spatially non-homogeneous surfactant concentrations. The critical parameters governing the displacement dynamics, such as contact angle and interfacial tension (IFT), can vary considerably due to spatially non-homogeneous surfactant concentrations. However, the influence of spatially non-homogeneous contact angle and IFT on the displacement in porous media has not been sufficiently explored. In this work, we visualized the surfactant flooding at different salinities in 2D micromodels. Our results suggest that the displacement patterns vary considerably with salinity. One displacement front was observed at low salinity, while displacement front separation occurred at intermediate and high salinity, forming two different displacement fronts. At intermediate and high salinity, we noticed that the downstream displacement was imbibition, while the upstream displacement was drainage. We suggest that the separation of displacement fronts was capillary-driven, arising from spatially non-homogeneous contact angle and IFT. Our study provides new insights into the effect of the phase behavior of surfactant/water/oil systems on displacement patterns.

Numerical studies of spontaneous imbibition in porous media: Model development and pore-scale perspectives

Spontaneous imbibition is a crucial two-phase flow process in a variety of subsurface and industrial applications. Due to the lack of an efficient and reliable pore-scale model, however, how pore-filling events in spontaneous imbibition influence average transport properties (i.e., capillary pressure and relative permeability curves) and entrapment of the nonwetting fluid has not been fully understood. In this work, we first experimentally verify an image-based dynamic pore-network model of spontaneous imbibition that is computationally efficient. Then, case studies of a Nubian sandstone are conducted. We demonstrate that average capillary pressure in cocurrent spontaneous imbibition is significantly overestimated by the widely used Young-Laplace equation. This is because the effects of dynamic pore-filling and air entrapment on average capillary pressure are not parameterized in the equation. Based on our pore-scale numerical results, we elaborate on the competition of pore-filling events under different viscosity ratios of the wetting to the nonwetting fluids. It is found that the filling mode evolves from the co-filling of neighboring pores to the preferential filling of small pores as the nonwetting viscosity increases. Our model will be a useful numerical tool for quantitatively predicting spontaneous imbibition in geological formations. Our findings will help us bridge the gap between pore-scale flow dynamics and the Darcy theory of spontaneous imbibition.

Fluid migration in low-permeability faults driven by decoupling of fault slip and opening

Understanding the response of faults to the injection of high-pressure fluids is important for several subsurface applications, for example, geologic carbon sequestration or energy storage. Lab-based experiments suggest that fluid injection can activate fault slip and that this slip can lead to increased fluid transmission along low-permeability faults. Here we present in situ observations from a cross-borehole fluid-injection experiment in a low-permeability shale-bearing fault, which show fault displacement occurring before fluid-pressure build-up. Comparing these observations with numerical models with differing permeability evolution histories, we find that the observed variation in fluid pressure is best explained by a change in permeability only after the fault fails and slips beyond the pressurized area. Once fluid migration occurs along the fault as a result of slip-induced permeability increase, the fault experiences further opening due to a decrease in the effective normal stress. We suggest that decoupling of fault slip and opening, leading to a rapid increase in fluid pressurization following the initial fault slip, could be an efficient driver for fluid migration in low-permeability faults. Decoupled fault slip and opening, leading to rapid fluid pressurization after initial failure, drives high-pressure fluid migration in low-permeability faults, according to modelling and in situ observations from a borehole fluid-injection experiment.

Kinetics of wettability alteration and droplet detachment from a solid surface by low-salinity: A lattice-Boltzmann method

The dynamics of droplet detachment from a surface is a fundamental topic studied in coating engineering, fluid mechanics, and subsurface engineering applications. This topic has direct relevance to wettability alteration using the modified ionic composition of water in contact with oil droplet, low salinity waterflooding (LSWF). Previous experimental studies of LSWF have shown a very long timescale in wettability alteration which cannot be explained using bulk diffusion coefficient. In the present study, we address both the time scale of detachment, as well as the impact of buoyancy and interfacial forces (referred to as Bond number) on droplet detachment by proposing an advanced GPU-enhanced lattice Boltzmann model. In this model, the immiscible two-phase flow has been coupled with wettability alteration due to the salinity dilution. After full validation of the model against the former experiments, a set of computational setups with distinct Bond numbers were designed to investigate the effect of interfacial tension and droplet size on the dynamics of droplet detachment. Results demonstrate that droplet detachment from a surface is not a unique function of Bond number and diffusion length scale is critical in detachment time. In summary, this study provides a fully validated model of LSWF and delineates the significant difference in impacts of interfacial tension and bubble size on detachment time, which can be used to predict capability for LSWF, to determine favorable conditions, as well as to observe and explain low-salinity-effect.

CO2 Foam Behavior in Carbonate Rock: Effect of Surfactant Type and Concentration

An understanding of how CO2 foam flows through a reservoir rock is useful for many subsurface applications, including enhanced oil recovery and CO2 storage. There are economic and environmental benefits in identifying surfactants that exhibit good foaming behavior with CO2 at both low concentrations and high foam qualities. Core flood experiments have been carried out to investigate the behavior of supercritical CO2 foams flowing through a high-permeability Indiana Limestone. The foaming behavior and concentration response of two surfactants, a betaine and a sultaine, were investigated. For the two surfactants, the transition foam quality and the maximum apparent foam viscosity both decreased with reducing surfactant concentration. A comparison between the foaming behaviors of these surfactants with CO2 and N2 was also carried out. It was found that the N2 generated stronger foam at low foam qualities, but the CO2 was better at maintaining good foaming behavior at high foam qualities.

Mobility of Sub-50 nm Iron Oxide Nanoparticles with Ultrahigh Initial Magnetic Susceptibility in Intact Berea Sandstone at High Salinity

Superparamagnetic iron oxide nanoparticles (IONPs), which have been investigated extensively as contrast-enhancing agents in biology, are being explored for subsurface applications such as electromagnetic tomography, fracture mapping, and enhanced oil recovery. However, two key challenges must be addressed: (a) high magnetic susceptibility and (b) colloidal stability and mobility under harsh reservoir conditions of high salinity and temperature. Herein, we synthesize IONPs grafted with poly(2-acrylamido-3-propanesulfonate-co-acrylic acid) poly(AMPS-co-AA) to achieve a high surface grafting density of polymer (49%) with minimal aggregation to yield sub-50 nm IONPs. The IONPs were found to be colloidally stable at 120 °C for a period of one month at pH 8. In crushed Berea sandstone, polymer-grafted IONPs exhibited significantly high mass breakthrough (84%) and low retention (149 μg/g) when used with a sacrificial polymer preflood (0.1% v/v). Intact Berea core experiments showed an 8-fold improvement in mass breakthrough (65%) and a two-thirds reduction in retention (from 433 μg/g to 160 μg/g) when compared to previous studies with IONPs synthesized via coprecipitation. The high grafting density of polymeric stabilizer and small nanoparticle size contribute to the improved mobility in consolidated porous media at high salinity.

Nanoparticle transport within non-Newtonian fluid flow in porous media.

Control over dispersion of nanoparticles in polymer solutions through porous media is important for subsurface applications such as soil remediation and enhanced oil recovery. Dispersion is affected by the spatial heterogeneity of porous media, the non-Newtonian behavior of polymer solutions, and the Brownian motion of nanoparticles. Here, we use the Euler-Lagrangian method to simulate the flow of nanoparticles and inelastic non-Newtonian fluids (described by Meter model) in a range of porous media samples and injection rates. In one case, we use a fine mesh of more than 3 million mesh points to model nanoparticles transport in a sandstone sample. The results show that the velocity distribution of nanoparticles in the porous medium is non-Gaussian, which leads to the non-Fickian behavior of nanoparticles dispersion. Due to pore-space confinement, the long-time mean-square displacement of nanoparticles depends nonlinearly on time. Additionally, the gradient of shear stress in the pore space of the porous medium dictates the transport behavior of nanoparticles in the porous medium. Furthermore, the Brownian motion of nanoparticles increases the dispersion of nanoparticles along the longitudinal and transverse direction.