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Bioaggregates are condensed porous materials comprising microbes, organic and inorganic matters, and water. They are commonly found in natural and engineered porous media and often cause clogging. Despite their importance, the formation mechanism of bioaggregates in porous media systems is largely unknown. Through microfluidic experiments and direct numerical simulations of fluid flow, we show that the rapid bioaggregation is driven by the interplay of the viscoelastic nature of biomass and hydrodynamic conditions at pore throats. At an early stage, unique flow structures around a pore throat promote the biomass attachment at the throat. Then, the attached biomass fluidizes when the shear stress at the partially clogged pore throat reaches a critical value. After the fluidization, the biomass is displaced and accumulated in the expansion region of throats forming bioaggregates. We further find that such criticality in shear stress triggers morphological changes in bioaggregates from rounded- to streamer-like shapes. This knowledge was used to control the clogging of throats by tuning the flow conditions: When the shear stress at the throat exceeded the critical value, clogging was prevented. The bioaggregation process did not depend on the detailed pore-throat geometry, as we reproduced the same dynamics in various pore-throat geometries. This study demonstrates that pore-throat structures, which are ubiquitous in porous media systems, induce bioaggregation and can lead to abrupt disruptions in flow., Proceedings of the National Academy of Sciences of the United States of America, 120 (14), ISSN:0027-8424, ISSN:1091-6490

Fluids with different densities often coexist in subsurface fractures and lead to variable-density flows that control subsurface processes such as seawater intrusion, contaminant transport, and geologic carbon sequestration. In nature, fractures have dip angles relative to gravity, and density effects are maximized in vertical fractures. However, most studies on flow and transport through fractures are often limited to horizontal fractures. Here, we study the mixing and transport of variable density fluids in vertical fractures by combining three-dimensional (3D) pore-scale numerical simulations and visual laboratory experiments. Two miscible fluids with different densities are injected through two inlets at the bottom of a fracture and exit from an outlet at the top of the fracture. Laboratory experiments show the emergence of an unstable focused flow path, which we term a “runlet.” We successfully reproduce an unstable runlet using 3D numerical simulations, and elucidate the underlying mechanisms triggering the runlet. Dimensionless number analysis shows that the runlet instability arises due to the Rayleigh-Taylor instability, and flow topology analysis is applied to identify 3D vortices that are caused by the Rayleigh-Taylor instability. Even under laminar flow regimes, fluid inertia is shown to control the runlet instability by affecting the size and movement of vortices. Finally, we confirm the emergence of a runlet in rough-walled fractures. Since a runlet dramatically affects fluid distribution, residence time, and mixing, the findings in this study have direct implications for the management of groundwater resources and subsurface applications.

Dissolution of carbonate rocks is a complex process in which the interplay of flow, transport, reaction, and geometric evolution plays an important role. The nonlinear couplings between these processes may lead to the formation of intricate patterns, including spontaneously formed channels (wormholes) [1]. It has been long established that the shapes of the dissolution patterns depend on fluid flow and mineral dissolution rates [2]. Recently, it also has become increasingly clear that pore-scale processes can impact large-scale morphologies [3,4]. However, the effects of pore-scale mixing on large-scale patterns remain unclear.In this work, we investigate the effect of pore-scale mixing processes on the evolution of dissolution channels. Pore space is represented by a network of cylindrical tubes with the diameter of each segment increasing in proportion to the local reactant consumption. The inlet concentration of each pore is controlled by local mixing rules. Two different mixing protocols are considered: full mixing, in which the incoming reactant fluxes are assumed to be completely mixed at the intersection, and streamline routing, where the tracer follows the streamlines into the outgoing pores. We found that streamline routing enhances the flow focusing particularly strongly in moderate Damköhler number regimes where relatively wide dissolution channels appear spontaneously in the system. With the same initial conditions as the full mixing case, the winning channels obtained with streamline routing not only propagate faster but also could grow at a different location in the system. The enhanced flow focusing caused by streamline routing produces thinner wormholes and leads to shorter breakthrough times. Lastly, the evolution of velocity distribution is also found to be distinctive depending on the mixing rule.[1] Hoefner, M. L. and Fogler, H. S. AIChE J. 34: 45–54, 1988[2] Golfier, F., et al. J. Fluid. Mech. 457: 213-254, 2002[3] Li, L., Peters, C. A., & Celia, M. A. Adv. Water Res., 29: 1351–1370, 2006[4] R. Roded, P. Szymczak, R. Holtzman, Geophys. Res. Lett. 48:e2021GL093659, 202

Mixing and reaction in rough channel flows govern various applications in engineering and natural processes such as microfluidic mixers and fracture flows, where channel wall roughness and flow inertia can vary widely. The combined effects of channel roughness and flow inertia induce complex flow structures such as recirculating flows, which along with diffusion–reaction processes, lead to a wide range of reactive transport behaviors. Currently, we lack a mechanistic understanding of mixing-induced reactive transport in rough channel flows. To establish a comprehensive understanding of bimolecular reactive transport in rough channel flows, we conduct a simulation-based study with varying channel roughness, Reynolds number (Re), and Peclet number (Pe). The simulation results reveal the distinctive effects of roughness, inertia (Re), and diffusion (Pe) on reactive transport. It is found that first passage time distributions between conservative and reactive tracers are significantly different, especially in mixing-limited pre-asymptotic regimes. The interplay between roughness and inertia leads to complex flow structures, which determines a spatially heterogeneous fluid stretching field. We show that the fluid stretching field together with solute diffusion leads to a spatially non-uniform chemical reactivity field, and the non-uniform chemical reactivity explains the distinctive transport behaviors between conservative and reactive tracers. Furthermore, we characterize the non-uniform reactivity with a reaction probability model that is parameterized with Lagrangian velocity magnitudes, and upscale reactive transport by incorporating the velocity-dependent reaction model into a spatial Markov model.

A visualization system was designed for a real-time observation of scales during direct contact membrane distillation (DCMD). The system was composed of a MD module, a camera and light sources. Firstly, visibility of the scales with the system and correlation between detected spots and wetting were assessed during the MD process using a polyvinylidene fluoride (PVDF) 0.45 μm membrane. The white spots were observed in the captured images from the visualization system and got broader with the electrical conductivity (EC) increase in permeate. It was verified that the spots indicated the pores blocked by the CaSO4 scales. The wetting occurred through the white spots if the pressure was applied. Therefore, the high wetting potential area could be identified with the suggested visualization system during the MD operation in real-time. The visualization system was applied to the MD process under different permeate temperatures (20 and 55 °C). The visualization system successfully detected the pore blocking related with different scale formation according to the temperatures at the membrane surface.

Aquifer storage and recovery (ASR) is an important water resources management technique that involves the injection of a large volume of water underground. For the successful implementation of an ASR project, a target aquifer should have a sufficient injection capacity, which is the maximum volume of water that can be safely injected. In nature, no aquitard is perfectly impermeable, and inter-aquifer leakage may have a major impact on injection capacity. Despite the importance of determining the injection capacity for ASR planning, there is no quantitative methodology that estimates the injection capacity of leaky aquifers. In this study, we first develop a solution for injection capacity with inter-aquifer leakage based on the Hantush - Jacob solution, and conduct a comprehensive sensitivity analysis to elucidate the influence of inter-aquifer leakage on injection capacity. From the sensitivity analysis, we show that inter-aquifer leakage can impact injection capacity by more than one order of magnitude, depending on the hydrogeological and operational parameters. We then develop a practical mapping methodology that estimates the injection capacity of leaky aquifers. We demonstrate the proposed methodology by applying it to a potential ASR site in Minnesota, USA, where ASR is considered as a solution to alleviate groundwater contamination by PFAS chemicals. The case study results reveal significant spatial variability in injection capacity over the study area and show an average increase in the injection capacity of about 26% compared to that in the nonleaky scenario. We also analyze the uncertainty in the estimated injection capacity due to the variability of aquitard properties and show that the variability of aquitard vertical hydraulic conductivity leads to a larger uncertainty in the estimated injection capacity than does the variability of aquitard thickness. This study elucidates the effects of inter-aquifer leakage on injection capacity and provides a practical methodology for injection capacity mapping.

We study the reactive displacement of two miscible fluids in channel flows and establish a quantitative link between fluid stretching and chemical reactivity. At the mixing interface, the two fluids react according to the instantaneous irreversible bimolecular reaction A+B→C . We simulate the advection–diffusion–reaction problem using a random walk based reactive particle method that is free of numerical dispersion. The relative contributions of stretching and diffusion to mixing-limited reaction is controlled by changing the Péclet number, and the channel roughness is also systematically varied. We observe optimal ranges of fluid stretching that maximize reactivity, which are captured by a Lagrangian stretching measure based on an effective time period that honours the stretching history. We show that the optimality originates from the competition between the enhanced mixing by fluid stretching and the mass depletion of the reactants. We analytically derive the spatial distribution of reaction products using a lamellar formulation and successfully predict the optimal ranges of fluid stretching, which are consistent across different levels of channel roughness. These findings provide a mechanistic understanding of how the interplay between fluid stretching, diffusion and channel roughness controls mixing-limited reactions in rough channel flows, and show how reaction hot spots can be predicted from the concept of optimal fluid stretching., S.Y. and P.K.K. acknowledge a grant from Korea Environment Industry and Technology Institute (KEITI) through Subsurface Environmental Management (SEM) Project (2020002440002), funded by the Korea Ministry of Environment (MOE). P.K.K. acknowledges MnDRIVE Advancing Industry, Conserving Our Environment at the University of Minnesota. M.D. acknowledges funding of the European Research Council (ERC) through the project MHetScale (contract number 617511), and the Spanish Research Agency (AEI) through the project HydroPore (contract number PID2019-106887GB-C31). We thank the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for computational resources and support.

We study how the complex interplay between roughness, inertia, and diffusion controls the tracer transport in rough channels. We first simulate the flow and tracer transport over wide ranges of channel roughness, Reynolds number ($Re$), and P\'eclet number ($Pe$) observable in nature. $Pe$ exerts a first-order control on first-passage time distributions, and the effect of roughness on the tracer transport becomes evident with the increase in $Re$. The interplay between the roughness and $Re$ causes eddy flows, which intensify or suppress anomalous transport depending on $Pe$. At infinite $Pe$, the late-time scaling follows a universal power-law scaling, which is explained by conducting a scaling analysis. With extensive numerical simulations and stochastic modeling, we show that the roughness, inertia, and diffusion effects are encoded in Lagrangian velocity statistics represented by velocity distribution and correlation. We finally predict the anomalous transport using a stochastic model that considers the Lagrangian velocity statistics.

Various hydrogeochemical processes can modify the quality of river water during riverbank filtration (RBF). Identifying the subsurface processes responsible for the bank-filtered water quality is challenging, but essential for predicting water quality changes and determining the necessity of post-treatment. However, no systematic approach for this has been proposed yet. In this study, the subsurface hydrogeochemical processes that caused the high concentrations of total iron (Fe) and sulfate (SO42−) in the bank-filtered water were investigated at a pilot-scale RBF site in South Korea. For this purpose, water quality variations were monitored in both the extraction well and the adjacent river over five months. The volumetric mixing ratio between the river water and the native groundwater in the RBF well was calculated to understand the effect of mixing on the quality of water from the well and to assess the potential contribution of subsurface reactions to water quality changes. To identify the subsurface processes responsible for the evolution of Fe and SO42− during RBF, an inverse modeling based on the chemical mass balance was conducted using the water quality data and the calculated volumetric mixing ratio. The modeling results suggest that pyrite oxidation by abundant O2 present in an unsaturated zone could be a primary process explaining the evolution of total Fe and SO42− during RBF at the study site. The presence of pyrite in the aquifer was indirectly supported by iron sulfate hydroxide (Fe(SO4)(OH)) detected in oxidized aquifer sediments.

We investigate large-scale particle motion and solute breakthrough in sparse three-dimensional discrete fracture networks characterized by power law distributed fracture lengths. The three networks we consider have the same fracture intensity values but exhibit different percolation densities, geometric properties, and topological structures. We considered two different average transport models to predict solute breakthrough, a streamtube model and a Bernoulli continuous time random walk model, both of which provide insights into the flow fields within the networks. The streamtube model provides acceptable predictions at short distances in two of the networks but fails in all cases to predict breakthrough times at the outlet plane, which indicates that particle motion in such fracture networks cannot be characterized by a constant velocity between the inlet and control plane at which the breakthrough curve is detected. Rather, the structure of the network requires that frequent velocity transitions be made as particles move through the system. Despite the relatively broad distribution of fracture radii and relatively small number of independent velocity transitions, the continuous time random walk approach conditioned on the initial velocity distribution provides reasonable predictions for the breakthrough curves at different distances from the inlet. The application of these averaged transport models provides a richer understanding of the link from the fracture network structure to flow and transport properties. ©2019. American Geophysical Union. All Rights Reserved., J. D. H. and A. H. are thankful for support from Department of Energy at Los Alamos National Laboratory under contract DE-AC52-06NA25 396 through the Laboratory-Directed Research and Development Program. J. D. H. acknowledges support from the LANL LDRD program office Grant 20180621ECR. M. D. gratefully acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). P. K. K. acknowledges a grant from Korea Environment Industry & Technology Institute(KEITI) through Subsurface Environmental Management (SEM) Project, funded by Korea Ministry of Environment(MOE; 2018002440003). dfnworks can be obtained at https://github.com/lanl/dfnWorks and run files for simulations can obtained at https://gitlab.com/hymanjd/ hyman-jgr-2018.git.

Biogeochemical reactions take place when reactants are present at the same location and time, which occurs through pore-scale mixing. Although the degree of mixing should be variable in space and time, most conventional reactive transport models do not consider mixing-dependent reaction rates and often make predictions based on rates measured with batch experiments. We quantify the effect of pore-scale mixing on the biodegradation rate of dissolved organic carbon (DOC) with sediment batch experiments, and study its potential impact on the field-scale fate and transport of DOC with numerical simulations. We collected sediment samples from an aquifer storage transfer and recovery (ASTR) field site located in Busan, South Korea, and conducted batch experiments to measure mixing-dependent biodegradation rates. Complete mixing conditions were realized by continuously shaking the batch, and diffusion-limited mixing conditions were realized by keeping the batch under static conditions. The two mixing conditions are most widely used conditions in batch experiments and they also represent the maximum and minimum degree of mixing. The different mixing conditions led to significant differences in the biodegradation rates (a factor of 4.9 on average). We then performed reactive transport modeling using the measured biodegradation rates to study the potential impact of pore-scale incomplete mixing on the field-scale DOC biodegradation. The results show that pore-scale mixing can significantly affect the effectiveness of biodegradation at the ASTR site. We generalize this finding by performing a comprehensive nondimensional sensitivity analysis of the fate and transport of DOC to pore-scale mixing conditions over a wide range of P e ´ clet and Damkohler numbers. We show that pore-scale incomplete mixing can be a major source of uncertainty in field-scale model predictions.

Structural parameter is a representative quantity of a porous medium, often used to explain mass transfer rate in membrane processes. Intrinsic structural parameter is defined using characteristic constants of a porous medium, and effective structural parameter is indirectly obtained by analyzing measured fluxes, especially in osmosis-driven processes. Although the two parameters are fundamentally equivalent, recent experimental studies show noticeable discrepancies between them. To resolve the inconsistency, we hypothesize that the fraction of effective membrane surface area should include interfacial porosity between the active layer and the porous substrate. To test our hypothesis, we develop a new pore-scale CFD (computational fluid dynamics) solver for both mass and momentum transfer in transient forward osmosis phenomena. Two pre-existing solvers of OpenFOAM, an open-source computational fluid dynamics software package, are combined to seamlessly link the coupled transport phenomena across the active layer, the support layer, and the crossflow zone. We defined a new structural parameter using the simulated flux and the interfacial porosity, first addressed in this study, and obtained an excellent agreement between our CFD results and published experimental data in the literature.

The injection and storage of freshwater in saline aquifers for the purpose of managed aquifer recharge is an important technology that can help ensure sustainable water resources. As a result of the density difference between the injected freshwater and ambient saline groundwater, the pressure field is coupled to the spatial salinity distribution, and therefore experiences transient changes. The effect of variable density can be quantified by the mixed convection ratio, which is a ratio between the strength of two convection processes: free convection due to the density differences and forced convection due to hydraulic gradients. We combine a density-dependent flow and transport simulator with an ensemble Kalman filter (EnKF) to analyze the effects of freshwater injection rates on the value-of-information of transient pressure data for saline aquifer characterization. The EnKF is applied to sequentially estimate heterogeneous aquifer permeability fields using real-time pressure data. The performance of the permeability estimation is analyzed in terms of the accuracy and the uncertainty of the estimated permeability fields as well as the predictability of breakthrough curve arrival times in a realistic push-pull setting. This study demonstrates that injecting fluids at a rate that balances the two characteristic convections can maximize the value of pressure data for saline aquifer characterization. Keywords: Managed aquifer recharge; Density-dependent flow; Inverse modeling; Ensemble Kalman filter; Value of information; Permeability estimation

We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes. © 2017 Elsevier Ltd., PKK and SL acknowledge a grant (16AWMP- B066761-04) from the AWMP Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government and the support from Future Research Program (2E27030) funded by the Korea Institute of Science and Technology (KIST). PKK and RJ acknowledge a MISTI Global Seed Funds award. MD acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). TLB acknowledges the support of European Research Council (ERC) through the project Re- activeFronts (648377). RJ acknowledges the support of the US Department of Energy through a DOE Early Career Award (grant DE-SC0009286). The data to reproduce the work can be obtained from the corresponding author.

Managing recharge of freshwater into saline aquifers requires accurate estimation of the heterogeneous permeability field for maximizing injection and recovery efficiency. Here, we present a methodology for subsurface characterization in saline aquifers that takes advantage of the density difference between the injected freshwater and the ambient saline groundwater. We combine high resolution forward modeling of density-driven flow with an efficient Bayesian geostatistical inversion algorithm. In the presence of a density difference between the injected and ambient fluids due to differences in salinity, the pressure field is coupled to the spatial distribution of salinity. This coupling renders the pressure field transient: the time evolution of the salinity distribution controls the density distribution which then leads to a time-evolving pressure distribution.We exploit this coupling between pressure and salinity to obtain an improved characterization of the permeability field without multiple pumping tests or additional salinity measurements. We show that the inversion performance improves with an increase in the mixed convection ratio–the relative importance between viscous forces from injection and buoyancy forces from density difference. Our work shows that measuring transient pressure data at multiple sampling points during freshwater injection into saline aquifers can be an effective strategy for aquifer characterization, key to the successful management of aquifer recharge.

Riverbank filtration is a commonly-used technology that improves water quality by passing river water through aquifers. In this study, a riverbank filtration site in Busan, South Korea, was investigated to understand the spatiotemporal evolution of high iron and sulfate concentrations observed in the riverbank-filtered water. Discrepancies between the nonreactive transport results and field measurements suggest that iron-sulfate-related geochemical reactions play a major role in the spatiotemporal evolution of the hydrochemical properties. Pyrite oxidation was hypothesized to be the main process driving the release of iron and sulfate. To test this hypothesis, a reactive transport model was developed, that implemented pyrite oxidation as a kinetic process and subsequent ferrous iron oxidation and ferric iron precipitation as equilibrium processes. The model accurately captured the temporal evolution of sulfate; however, iron concentrations were underestimated. Sensitivity tests revealed that adjusting reaction constants significantly improved the prediction of iron concentrations. The results of this study suggest that pyrite oxidation can affect the hydrochemistry of riverbank-filtered water and highlight the potential limitations of using theoretical reaction constants in field modeling applications.

We study how the interplay between fracture aperture heterogeneity and tracer injection mode controls fluid flow and tracer transport in three‐dimensional (3D) discrete fracture networks (DFNs). The direct 3‐D DFN simulations show that tracer injection mode has substantial effects on tracer spreading across all levels of aperture heterogeneity. The key controlling factor for effective transport is the initial Lagrangian velocity distribution, which is determined by the interplay between injection mode and aperture heterogeneity. The fundamental difference between initial Lagrangian velocity distribution and domain‐scale Eulerian velocity distribution plays a vital role in determining anomalous transport. We effectively capture the observed anomalous transport using an upscaled transport model that incorporates initial velocity distribution, stationary velocity distribution, velocity correlation length, and average advective tortuosity. With the upscaled transport model, we accurately capture the evolution of Lagrangian velocity distribution and predict longitudinal spreading in 3‐D DFN., PKK acknowledges the College of Science & Engineering at the University of Minnesota and the George and Orpha Gibson Endowment for its generous support of Hydrogeology. PKK and WSH acknowledge a grant from Korea Environment Industry and Technology Institute (KEITI) through Subsurface Environmental Management (SEM) Project, funded by the Korea Ministry of Environment (MOE) (2018002440003). MD acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511), and the support of the Spanish Ministry of Science and Innovation through a Severo Ochoa project (No. CEX2018324 000794‐S), and the project HydroPore (PID2019‐106887GB‐C31). JDH acknowledges support from the LANL LDRD program office Grant 20180621ECR and thanks to the Department of Energy (DOE) Basic Energy Sciences program (LANLE3W1) for support as well.

We show the emergence of reaction hotspots induced by three-dimensional (3D) vortices with a simple $A+B \rightarrow C$ reaction. We conduct microfluidics experiments to visualize the spatial map of the reaction rate with the chemiluminescence reaction and cross-validate the results with direct numerical simulations. 3D vortices form at spiral saddle type stagnation points, and the 3D vortex flow topology is essential for initiating reaction hotspots. The effect of vortices on mixing and reaction becomes more vigorous for rough-walled channels, and our findings are valid over wide ranges of channel dimensions and Damk\"{o}hler numbers., Comment: 5 pages, 4 figures, manuscript under review

We study first passage behaviors in the flow through three-dimensional random fracture networks. Network and flow heterogeneity lead to the emergence of heavy-tailed first passage time distributions that evolve with increasing distance between the start and target planes, and transition toward stable laws. Analysis of the spatial memory of the first passage process shows that particle motion can be quantified stochastically by a time domain random walk conditioned on the initial velocity data. This approach identifies advective tortuosity, the velocity point distribution and the average fracture link length as key quantities for the prediction of first passage times. Using this approach, we develop a theory for the evolution of first passage times with increasing distance between the start and target planes and the convergence towards stable laws. © 2019 American Physical, J.D.H. and A.H. are thankful for support from the US Department of Energy through the Los Alamos National Laboratory. Specifically, support through the Laboratory-Directed Research and Development Program grants 20180621ECR and 20170103DR. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001). J.D.H. also thanks the partial support of DOE’s Office of Science Basic Energy Sciences E3W1. M.D. gratefully acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). P.K.K. acknowledges a grant from the Korea Environment Industry & Technology Institute (KEITI) through Subsurface Environmental Management (SEM) Project, funded by the Korea Ministry of Environment (MOE) (2018002440003).

Precipitates induced by the pore-scale mixing of iron sulfate solutions with simulated groundwater were investigated using a microfluidic pore model to assess the environmental impacts of the infiltration of acid mine drainage into a shallow aquifer. This model was employed to visualize the formation of precipitates in a porous network and to evaluate their physicochemical influences on pore flow. Four types of groundwater (Na–HCO3, Na–SO4, Na–Cl, and Ca–Cl) were evaluated, and precipitation rates were calculated by processing images of precipitates in the pores captured via microscopy. The results showed that all groundwater types formed a yellow-brownish precipitate at the interface of the iron solution and simulated groundwater flow. Microscopic X-ray analyses demonstrated that precipitate morphology varied with groundwater type. Faster precipitation was observed in the following order by groundwater type: Na–HCO3 > Na–Cl > Na–SO4 > Ca–Cl, which was attributed to the different stability constants of the major anions in each simulated groundwater with Fe ions. Chemical equilibrium models suggested that precipitates were Fe minerals, with FeOOH as the predominant form consistent with the results of X-ray photoelectron spectrometry. The presence of FeOOH implies that precipitates may serve as an effective sorption barrier against some nutrients and heavy metals for the underlying groundwater. However, dye-flow experiments suggested that the precipitates may clog aquifer pores, thereby altering hydrogeological properties in the aquifer.

We report the emergence of anomalous (non-Fickian) transport through a rough-walled fracture as a result of increasing normal stress on the fracture. We show that the origin of this anomalous transport behavior can be traced to the emergence of a heterogeneous flow field dominated by preferential channels and stagnation zones, as a result of the larger number of contacts in a highly stressed fracture. We show that the velocity distribution determines the late-time scaling of particle spreading, and velocity correlation determines the magnitude of spreading and the transition time from the initial ballistic regime to the asymptotic anomalous behavior. We also propose a spatial Markov model that reproduces the transport behavior at the scale of the entire fracture with only three physical parameters. Our results point to a heretofore unrecognized link between geomechanics and particle transport in fractured media.

Pumping tests are routinely performed to characterize aquifers. In particular, recovery tests involve analyzing the residual drawdown after the termination of pumping. Evaluating the radius of investigation of pumping tests is important for test design and interpretation. However, the radius of investigation during the recovery phase has rarely been discussed in the literature. Here, we derive exact (semi-analytical) and approximate (fully analytical) solutions for the radius of investigation during the recovery phase, and present a sensitivity analysis for typical parameter values. During recovery, the radius of investigation shows non-monotonic behavior: it first increases, then decreases, and eventually vanishes. The maximum radius can be up to five times larger than that at the end of the pumping phase in the investigated range of parameter values. However, the conditions yielding a large radius of investigation are such that the time at which the maximum occurs is relatively large: up to one hundred times the pumping duration, depending on the parameters. Thus, in practice, one may need to find a compromise between the size of the investigated area and the time required for the test. In addition to the aquifer parameters, we observe that the pumping rate and duration, and the apparent measurement resolution are key parameters controlling the radius of investigation during recovery. Finally, the approach is applied to a field example. This study provides fundamental insight on the radius of investigation of recovery tests as well as practical solutions, thereby facilitating the design and interpretation of recovery tests in practice.

Prior to determining the optimal operating parameters for CO2 injection, conditions for both injection wellbore and storage formation should be evaluated; the build-up pressure induced by the CO2 injection could promote fractures in the storage formation, even collapsing the wellbore. In this study, a hybrid optimization methodology, which combined the proxy modeling and multi-objective optimization, was engaged in searching appropriate operating conditions for CO2 injection. The study utilized a fully coupled wellbore-reservoir (WR) model to simulate the CO2 injection scenarios. Three responses, such as pressure, temperature, and CO2 mass flow rate at the bottom-hole of injection wellbore, were investigated. To reduce the computational cost, the statistical proxy models were developed for approximating three responses. The developed fine-tuned proxy models revealed four influential factors; wellhead pressure, injected CO2 temperature, wellbore diameter, and permeability of a storage formation were significant in predicting three responses. Among these four influential factors, permeability was treated to be an uncertainty factor, while the other three factors were treated as tuning factors. According to acquired optimal solution sets, the optimum values for wellhead pressure and injected CO2 temperature were distributed around 10.0 MPa and 35 °C, respectively. For the wellbore diameter, its mean of optimal solutions was 0.1 m, and more solutions were concentrated at this mean value with a decrease in permeability.

Channel meanders in rivers induce complex three-dimensional (3D) flow characteristics such as secondary flows and flow recirculation. Helical secondary flows promote transverse dispersion, and flow recirculation zones trap tracers. As a result, these meander-driven flows may cause anomalous transport manifested by unusually elevated levels of tracer concentration at early and late times of breakthrough curves (BTCs). In this study, we perform 3D numerical simulations in meandering channels across a wide range of channel sinuosity to investigate the impact of meander geometry on flow and transport. We solve 3D Reynolds-averaged Navier-Stoke equations blended with a SST k − ω turbulence model to obtain velocity and turbulence fields. We then incorporate the obtained flow fields into a Lagrangian particle tracking model to simulate solute transport. Sinuosity higher than 1.5 leads to the onset of horizontal recirculating flows along the apex outer banks, and these recirculation zones expand as sinuosity increases. The analysis of the transport simulations elucidates that the interplay between the secondary flows and recirculation zones induces anomalous transport. The helical secondary flows bring particles into the recirculation zones by promoting transverse dispersion, and the recirculating flows delay particle transport by the trapping effect. We show that the tail power-law slope and truncated time of BTCs as well as Lagrangian tortuosity distributions change dramatically with the emergence of recirculation zones. These analyses demonstrate that the recirculation zones are acting as the primary driver of anomalous transport. Finally, we successfully predict the observed anomalous transport with a Spatial Markov Model (SMM), which is an upscaled transport model that incorporates Lagrangian velocity distribution and spatial velocity correlation. The successful predictions show that the Lagrangian velocity statistics effectively capture the underlying mechanisms of anomalous transport in meandering open-channel flows.

We investigate the effects of geological stress on fluid flow and tracer transport in natural fracture networks. We show the emergence of non-Fickian (anomalous) transport from the interplay among fracture network geometry, aperture heterogeneity, and geological stress. In this study, we extract the fracture network geometry from the geological map of an actual rock outcrop, and we simulate the geomechanical behavior of fractured rock using a hybrid finite-discrete element method. We analyze the impact of stress on the aperture distribution, fluid flow field, and tracer transport properties. Both stress magnitude and orientation have strong effects on the fracture aperture field, which in turn affects fluid flow and tracer transport through the system. We observe that stress anisotropy may cause significant shear dilation along long, curved fractures that are preferentially oriented to the stress loading. This, in turn, induces preferential flow paths and anomalous early arrival of tracers. An increase in stress magnitude enhances aperture heterogeneity by introducing more small apertures, which exacerbates late-time tailing. This effect is stronger when there is higher heterogeneity in the initial aperture field. To honor the flow field with strong preferential flow paths, we extend the Bernoulli Continuous Time Random Walk model to incorporate dual velocity correlation length scales. The proposed upscaled transport model captures anomalous transport through stressed fracture networks and agrees quantitatively with the high-fidelity numerical simulations. © 2019. American Geophysical, P. K. K. acknowledges the College of Science and Engineering at the University of Minnesota and the George and Orpha Gibson Endowment for its generous support of Hydrogeology. P. K. K. also acknowledges a grant from Korea Environment Industry and Technology Institute (KEITI) through Subsurface Environmental Management (SEM) Project, funded by Korea Ministry of Environment (MOE) W12530(2018002440003). M. D. acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). R. J. acknowledges funding by the U.S. Department of Energy Office of Science (grant DE-SC0018357). The digitized Bristol fracture network data are freely available through a data repository system at the University of Minnesota (https://doi.org/10.13020/ah7v-6n67).

Seismic interpretation of subsurface structures is traditionally performed without any account of flow behavior. Here we present a methodology for characterizing fractured geologic reservoirs by integrating flow and seismic data. The key element of the proposed approach is the identification—within the inversion—of the intimate relation between fracture compliance and fracture transmissivity, which determine the acoustic and flow responses of a fractured reservoir, respectively. Owing to the strong (but highly uncertain) dependence of fracture transmissivity on fracture compliance, the modeled flow response in a fractured reservoir is highly sensitive to the geophysical interpretation. By means of synthetic models, we show that by incorporating flow data (well pressures and tracer breakthrough curves) into the inversion workflow, we can simultaneously reduce the error in the seismic interpretation and improve predictions of the reservoir flow dynamics. While the inversion results are robust with respect to noise in the data for this synthetic example, the applicability of the methodology remains to be tested for more complex synthetic models and field cases.

Pore-scale velocity and turbulence structures near streambeds may control solute transport and dispersion in streams. In this study, pore-scale flow and transport simulations are performed to investigate the effects of pore-scale processes on anomalous transport, which is often manifested by remarkably long residence times of solute particles, in coupled free-flow and porous media systems. By solving the 2D Reynolds-averaged Navier–Stokes equations integrated with a renormalization group k − e turbulence model, we resolve complex pore-scale flows featured by vortices and preferential flows. Then, we incorporate the simulated velocity and turbulence fields into a Lagrangian particle tracking model that considers advection, turbulent diffusion, and molecular diffusion. Simulation results reveal that the interplay between pore-scale vortices and turbulence structures near the free-flow-permeable bed interface controls anomalous transport. High porosity induces strong turbulence penetration and preferential flow paths within permeable beds. The enhanced subsurface turbulence facilitates the escape of solute particles from recirculation zones via turbulent diffusion, causing steep power-law slopes in breakthrough curves (BTCs). In contrast, low porosity introduces heavy tailing in BTCs from particles that are trapped in the near-interface recirculation zones characterized by low velocities and limited turbulence. We upscale and predict particle transport via a Spatial Markov model (SMM) honoring the interplay between Lagrangian velocity distribution and velocity correlation. The SMM reproduces anomalous transport behaviors obtained from the numerical simulations. These results demonstrate that Lagrangian velocity statistics effectively encode anomalous transport mechanisms in the coupled systems.

Predicting variable-density flow and transport in aquifers is critical for the management of many coastal saline aquifers. Accurate characterization of hydrogeological parameters is critical for prediction, and the characterization is often conducted by assimilating data into models. However, few studies have investigated the underlying physics controlling the value-of-information (VOI) of data for aquifer characterization. In this study, we show how a greater understanding of the underlying physics controlling pressure and concentration data coupling can lead to improved characterization. In variable-density flow, the key physics that controls the VOI of pressure and concentration data is the non-linear coupling between flow and transport via fluid density which causes the pressure field to experience transient changes according to the evolution of salinity distribution. We first demonstrate the coupling between pressure and concentration data using information theory, and then systematically investigate how the variable-density flow impacts the VOI of these data in relation to permeability estimation. Using an ensemble Kalman filter, we estimate the permeability field of saline aquifer systems in two scenarios of data usage: pressure data only, and pressure and concentration data jointly. This study demonstrates that, regardless of the data usage scenario, the maximum VOI of data is obtained when free convection and forced convection are balanced. We further show that the advantage of joint inversion of pressure and concentration data decreases as the coupling effect between flow and transport increases. Finally, we study how the level of permeability field heterogeneity affects the coupling, which in turn controls VOI of pressure and concentration data.

We study the nature of non-Fickian particle transport in 3-D porous media by simulating fluid flow in the intricate pore space of real rock. We solve the full Navier-Stokes equations at the same resolution as the 3-D micro-CT (computed tomography) image of the rock sample and simulate particle transport along the streamlines of the velocity field. We find that transport at the pore scale is markedly anomalous: longitudinal spreading is superdiffusive, while transverse spreading is subdiffusive. We demonstrate that this anomalous behavior originates from the intermittent structure of the velocity field at the pore scale, which in turn emanates from the interplay between velocity heterogeneity and velocity correlation. Finally, we propose a continuous time random walk model that honors this intermittent structure at the pore scale and captures the anomalous 3-D transport behavior at the macroscale.

We develop a continuous time random walk (CTRW) approach for the evolution of Lagrangian velocities in steady heterogeneous flows based on a stochastic relaxation process for the streamwise particle velocities. This approach describes persistence of velocities over a characteristic spatial scale, unlike classical random walk methods, which model persistence over a characteristic time scale. We first establish the relation between Eulerian and Lagrangian velocities for both equidistant and isochrone sampling along streamlines, under transient and stationary conditions. Based on this, we develop a space continuous CTRW approach for the spatial and temporal dynamics of Lagrangian velocities. While classical CTRW formulations have non-stationary Lagrangian velocity statistics, the proposed approach quantifies the evolution of the Lagrangian velocity statistics under both stationary and non-stationary conditions. We provide explicit expressions for the Lagrangian velocity statistics, and determine the behaviors of the mean particle velocity, velocity covariance and particle dispersion. We find strong Lagrangian correlation and anomalous dispersion for velocity distributions which are tailed toward low velocities as well as marked differences depending on the initial conditions. The developed CTRW approach predicts the Lagrangian particle dynamics from an arbitrary initial condition based on the Eulerian velocity distribution and a characteristic correlation scale., MD and AC acknowledge the support of the European Research Council (ERC) through the project MHetScale (617511)

This study formulates and analyzes continuous time random walk (CTRW) models in radial flow geometries for the quantification of non-local solute transport induced by heterogeneous flow distributions and by mobile-immobile mass transfer processes. To this end we derive a general CTRW framework in radial coordinates starting from the random walk equations for radial particle positions and times. The particle density, or solute concentration is governed by a non-local radial advection-dispersion equation (ADE). Unlike in CTRWs for uniform flow scenarios, particle transition times here depend on the radial particle position, which renders the CTRW non-stationary. As a consequence, the memory kernel characterizing the non-local ADE, is radially dependent. Based on this general formulation, we derive radial CTRW implementations that (i) emulate non-local radial transport due to heterogeneous advection, (ii) model multirate mass transfer (MRMT) between mobile and immobile continua, and (iii) quantify both heterogeneous advection in a mobile region and mass transfer between mobile and immobile regions. The expected solute breakthrough behavior is studied using numerical random walk particle tracking simulations. This behavior is analyzed by explicit analytical expressions for the asymptotic solute breakthrough curves. We observe clear power-law tails of the solute breakthrough for broad (power-law) distributions of particle transit times (heterogeneous advection) and particle trapping times (MRMT model). The combined model displays two distinct time regimes. An intermediate regime, in which the solute breakthrough is dominated by the particle transit times in the mobile zones, and a late time regime that is governed by the distribution of particle trapping times in immobile zones. These radial CTRW formulations allow for the identification of heterogeneous advection and mobile-immobile processes as drivers of anomalous transport, under conditions relevant for field tracer tests. © 2015 Elsevier Ltd., MD acknowledges the support of the European Research Council (ERC) through the project MHetScale (contract number 617511). We thank Dr. Antoine Aubenau and two anonymous reviewers for their comments.

Geologic model building of the subsurface is a complicated and lengthy process. Seismic data are usually inverted at the beginning of this process and discarded after inversion and interpretation. Modifications from the seismic inversion results are almost never validated due to the one-way information flow towards reservoir properties. In this paper, we propose to include the production data and the inverted reservoir properties back in the seismic inversion during the next iteration of geologic model updates. The elastic properties and the fluid-flow properties are connected and cross-updated based on the underlying rock physics models. The statistical approach we propose in this paper not only provides a single Earth model that best satisfies both types of data, but also has the potential to properly quantify the uncertainty associated with this model. We show preliminary results from the proposed framework on a synthetic gas-pocket identification example.

We investigate transport on regular fracture networks that are characterized by heterogeneity in hydraulic conductivity. We discuss the impact of conductivity heterogeneity and mixing within fracture intersections on particle spreading. We show the emergence of non-Fickian transport due to the interplay between the network conductivity heterogeneity and the degree of mixing at nodes. Specifically, lack of mixing at fracture intersections leads to subdiffusive scaling of transverse spreading but has negligible impact on longitudinal spreading. An increase in network conductivity heterogeneity enhances both longitudinal and transverse spreading and leads to non-Fickian transport in longitudinal direction. Based on the observed Lagrangian velocity statistics, we develop an effective stochastic model that incorporates the interplay between Lagrangian velocity correlation and velocity distribution. The model is parameterized with a few physical parameters and is able to capture the full particle transition dynamics. © 2015 American Physical Society., PKK and RJ acknowledge the support of the US Department of Energy (grant DE-SC0003907), and a MISTI Global Seed Funds award. MD acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). TLB acknowledges the support of the INTERREG IV project CLIMAWAT. The data to reproduce the work can be obtained from the corresponding author

Flow and transport through fractured geologic media often leads to anomalous (non-Fickian) transport behavior, the origin of which remains a matter of debate: whether it arises from variability in fracture permeability (velocity distribution), connectedness in the flow paths through fractures (velocity correlation), or interaction between fractures and matrix. Here we show that this uncertainty of distribution- versus correlation-controlled transport can be resolved by combining convergent and push-pull tracer tests because flow reversibility is strongly dependent on velocity correlation, whereas late-time scaling of breakthrough curves is mainly controlled by velocity distribution. We build on this insight, and propose a Lagrangian statistical model that takes the form of a continuous time random walk (CTRW) with correlated particle velocities. In this framework, velocity distribution and velocity correlation are quantified by a Markov process of particle transition times that is characterized by a distribution function and a transition probability. Our transport model accurately captures the anomalous behavior in the breakthrough curves for both push-pull and convergent flow geometries, with the same set of parameters. Thus, the proposed correlated CTRW modeling approach provides a simple yet powerful framework for characterizing the impact of velocity distribution and correlation on transport in fractured media. © 2014. American Geophysical Union. All Rights Reserved., The authors thank Nicolas Lavenant and Rebecca Hochreutener for their help in setting up and running the field experiments. Funding for this work was provided by the INTERREG IV project CLIMAWAT, the national network of hydrogeological sites H+ (hplus.ore.fr). MD acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). RJ acknowledges the support of the US Department of Energy (grant DE-SC0003907), and a MISTI Global Seed Funds award.

Microporous polypropylene membrane filters are modified with a cationic surfactant, dimethyldioctadecylammonium bromide (DDAB), to create a charged surface. Negatively charged nanoparticles can then be filtered by utilizing the electrostatic interaction between the charged particles and the polar heads of the surfactants adsorbed on the filters. The study focuses on particle adsorption and particle filtration. Particle adsorption studies have shown that particle adsorption can be increased significantly with this treatment due to Coulombic attraction. Filtration results for 60 nm negatively charged particles (carboxylate modified copolymer) filtered through DDAB treated filters show a significant increase in particle capture efficiency. Capture efficiency is dependent on the pH of the suspension due to competitive adsorption between the hydroxide ions and the negatively charged nanoparticles. As the mean pore diameter increases, capture efficiency decreases due to the increase in distance between the part...