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Groundwater monitoring networks are direct sources of information for revealing subsurface system dynamic processes. However, designing such networks is difficult due to uncertainties in the spatial heterogeneity of aquifer parameters such as permeability (k). This study combines deep learning and information theory with an optimization framework to address network design problems in heterogeneous aquifer systems. The framework first employs a generative adversarial network to parameterize heterogeneous k distribution using a low‐dimensional latent representation. Then, surrogate models are developed based on the deep neural networks to perform uncertainty quantification of pressure heads and solute concentrations at locations of pre‐designed candidate monitoring stations. The monitoring stations are then ranked using the greedy search algorithm based on the maximum information minimum redundancy (MIMR) criterion. In order to depict the importance of each candidate monitoring location, the hotspot maps of the selection probability (Ps) are derived from MIMR repetition results. Comprehensive monitoring networks derived from the hotspot maps are then conducted as the final monitoring stations to improve monitoring information compared to MIMR results. Additionally, nine entropy quantization strategies are compared to evaluate their effects on monitoring network optimization results. Results indicate that caution should be taken when selecting entropy quantization strategies to achieve the accuracy required for model calibration and to improve the efficiency of monitoring optimization. Considering high‐dimensional uncertainties associated with aquifer parameters, the developed framework can provide important insights for monitoring network designs in various earth observational projects. Plain Language Summary: Groundwater is widely distributed throughout the crust and lithosphere playing a critical role in geological evolution processes and environmental problems. This study presents an integrated framework for designing groundwater monitoring networks in heterogeneous aquifer systems. The proposed model takes advantage of the strength of deep‐learning algorithms and deals with high‐dimensional and highly non‐linear problems of entropy‐based methods for designing the monitoring networks. The framework can efficiently provide monitor locations for complex aquifers. The monitoring data collected by the designed network can significantly reduce uncertainties associated with high‐dimensional parameters and contain the least amount of redundant information. Further, the proposed method is significant in practice as credible groundwater forecasting requires model calibrations that use monitoring data. Key Points: Effective designed monitoring networks are critical for reducing model uncertainties and properly reflecting subsurface dynamic processesDeep learning and maximum information minimum redundancy criterion were combined to design optimal monitoring networks for heterogeneous aquifersThe designed monitoring networks were verified to be influenced by different entropy quantification strategies [ABSTRACT FROM AUTHOR]

We investigate mechanisms that enhance lateral methane (CH4) plume migration in shallow aquifers that exhibit complex and multiscale sedimentary architecture. We show how heterogeneity in capillary pressure characteristics related to the sedimentary architecture causes gaseous CH4 to spread over larger areas by retarding, deviating, or blocking upward buoyancy‐driven CH4 migration. Simplifying or ignoring capillary pressure heterogeneity thus leads to overestimation of leaked CH4 to the atmosphere, and underestimation of mobile gaseous CH4 in aquifer. We show, both qualitatively and quantitatively, that meter‐scale sedimentary stratification contributes more to CH4 plume migration than the millimeter‐ and centimeter‐scale strata comprising them. Results indicate that the extent of gaseous CH4 leakage, and its associated impacts on groundwater quality and global warming, cannot be accurately assessed unless the sedimentary architecture and resulting heterogeneity in capillary pressure are represented. Plain Language Summary: Increasing levels of methane (CH4) in shallow aquifers from both natural and anthropogenic sources have become a serious concern worldwide. Leaked CH4 not only contaminates drinking water resources, resulting in long‐term adverse health effects, but may also enter the atmosphere, accelerating global climate change. To assess the potential risks associated with CH4 leakage and design effective mitigation strategies, it is crucial to understand how geological features at different spatial scales affect the behavior of CH4 in groundwater. Field experiments have shown that horizontal migration of CH4 is beyond that expected due to groundwater flow alone, but the mechanism driving these phenomena is unknown. In this work, we show that this unexpected behavior may be caused by geological heterogeneity and the resulting heterogeneity in capillary pressure, which is the pressure difference at the interface between gaseous CH4 and groundwater. Key Points: Capillary heterogeneity in relatively homogenous and shallow aquifers places leading‐order controls on lateral CH4 plume spreadingNeglecting capillary heterogeneity leads to overestimation of atmospheric CH4 and underestimation of mobile gaseous CH4 in aquiferMeter‐scale sedimentary stratification contributes more to CH4 plume migration than millimeter‐ and centimeter‐scale features [ABSTRACT FROM AUTHOR]

Groundwater is a primary source of drinking water worldwide, but excess nutrients and emerging contaminants could compromise groundwater quality and limit its usage as a drinking water source. As such contaminants become increasingly prevalent in the biosphere, a fundamental understanding of their fate and transport in groundwater systems is necessary to implement successful remediation strategies. The dynamics of surface water‐groundwater (hyporheic) exchange within a glacial, buried‐valley aquifer system are examined in the context of their implications for the transport of nutrients and contaminants in riparian sediments. High conductivity facies act as preferential flow pathways which enhance nutrient and contaminant delivery, especially during storm events, but transport throughout the aquifer also depends on subsurface sedimentary architecture (e.g. interbedded high and low conductivity facies). Temperature and specific conductance measurements indicate extensive hyporheic mixing close to the river channel, but surface water influence was also observed far from the stream‐aquifer interface. Measurements of river stage and hydraulic head indicate that significant flows during storms (i.e., hot moments) alter groundwater flow patterns, even between consecutive storm events, as riverbed conductivity and, more importantly, the hydraulic connectivity between the river and aquifer change. Given the similar mass transport characteristics among buried‐valley aquifers, these findings are likely representative of glacial aquifer systems worldwide. Our results suggest that water resources management decisions based on average (base) flow conditions may inaccurately represent the system being evaluated, and could reduce the effectiveness of remediation strategies for nutrients and emerging contaminants. [ABSTRACT FROM AUTHOR]

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Tides in coastal rivers drive river‐groundwater (hyporheic) exchange and provide opportunities for nitrate removal that may improve coastal water quality. Silt and sand layers in coastal floodplain sediments can alter the flow and transformation of nitrate. Our goal was to understand how sediment heterogeneity influences nitrogen dynamics near tidal rivers. Numerical simulations show that oxic, variably saturated sand layers and anoxic, organic‐rich silt layers are sites of nitrification and denitrification, respectively. The exchange of river water and nitrate through heterogeneous sediments increases with sand fraction, as sand lenses become longer and more connected. The amount of nitrate removed from river water also increases but represents a smaller portion of total nitrate exchange through the hyporheic zone, causing removal efficiency to decline. Our results suggest that accurate characterization of aquifer heterogeneity leads to an improved understanding of sites of nutrient transformation within floodplain sediments. Plain Language Summary: Excess nitrate can degrade coastal water quality, but microbial reactions reduce its concentration within the bed and banks of tidal rivers, where surface water and groundwater mix. Spatial arrangements of different sediments (sand and mud) affect the mixing of river water and groundwater and thus affect nitrate removal. Here, we use computer models to simulate nitrate transformation along a tidal river with different amounts of coarse and fine sediments. Coarse sediments promote groundwater flow and nitrate production, while fine sediments promote nitrate removal. The amount of nitrate removed from river water is greater in aquifers with coarser sediments, but the removal efficiency decreases. Removal also varies with the spatial distribution of sand and mud in sediments but to a lesser degree. Computer models of nitrate transport should consider the distribution of different sediment types. Key Points: Tidal aquifers are a net sink of nitrate; removal magnitude varies with permeability structureSediment organic matter is a local source of dissolved organic carbon driving small‐scale patterns of nitrate transformationNitrification dominates in sand layers, where oxygen and nutrients are mobile; denitrification dominates in organic‐rich, silt layers [ABSTRACT FROM AUTHOR]

In coastal rivers, tides facilitate surface water‐groundwater exchange and strongly coupled nitrification‐denitrification near the fluctuating water table. We used numerical fluid flow and reactive transport models to explore hydrogeologic and biogeochemical controls on nitrogen transport along an idealized tidal freshwater zone based on field observations from White Clay Creek, Delaware, USA. The capacity of the riparian aquifer to remove nitrate depends largely on nitrate transport rates, which initially increase with increasing tidal range but then decline as sediments become muddier and permeability decreases. Over the entire model reach, local nitrification provides a similar amount of nitrate as surface and groundwater contributions combined. More than half (~66%) of nitrate removed via denitrification is produced in situ, while the vast majority of remaining nitrate removed comes from groundwater sources. In contrast, average nitrate removal from surface water due to tidal pumping amounts to only ~1% of the average daily in‐channel riverine nitrate load or 1.77 kg of nitrate along the reach each day. As a result, tidal bank storage zones may not be major sinks for nitrate in coastal rivers but can act as effective sinks for groundwater nitrate. By extension, tidal bank storage zones provide a critical ecosystem service, reducing contributions of groundwater nitrate, which is often derived from septic tanks and fertilizers, to coastal rivers. Plain Language Summary: Nitrate is one of the most common pollutants and can degrade coastal water quality. In the beds and banks of coastal rivers, tides increase the mixing of river and groundwater, which creates opportunities for nitrate production by nitrification and removal by denitrification. It is unknown which process prevails along tidal rivers and how these processes vary in strength as sediments become muddier and tides become larger near the coast. In this study, we used computer models to estimate rates of nitrate production and removal and where they are greatest along tidal rivers. We found that nitrate is ultimately removed from the aquifer, but that the majority of nitrate removed is from nitrification near the water table and groundwater sources. Although aquifers along tidal rivers are only marginally effective at removing nitrate from river water, their ability to remove groundwater nitrate is vital for maintaining coastal water quality. Key Points: Surface water‐groundwater exchange decreases moving downstream, as sediment permeability decreases, despite increasing tidal rangeThe majority of denitrification removes nitrate produced in the riparian zone, with groundwater supplying the remaining nitrate removedTidal bank storage zones along rivers can be an effective sink for groundwater nitrate, mitigating nitrate export to coasts [ABSTRACT FROM AUTHOR]

Yucca Mountain, Nevada, had been extensively investigated as a potential deep geologic repository for storing high-level nuclear wastes. Previous field investigations of stratified alluvial aquifer downstream of the site revealed that there is a hierarchy of sedimentary facies types. There is a corresponding log conductivity and reactive surface area subpopulations within each facies at each scale of sedimentary architecture. Here we use a Lagrangian-based transport model in order to analyze radionuclide dispersion in the saturated alluvium of Fortymile Wash, Nevada. First, we validate the Lagrangian model using high-resolution flow and reactive transport simulations. Then, we used the validated model to investigate how each scale of sedimentary architecture may affect long-term radionuclide transport at Yucca Mountain. Results show that the reactive solute dispersion developed by the Lagrangian model matches the ensemble average of numerical simulations well. The link between the alluvium spatial variability and reactive solute dispersion at different spatiotemporal scales is demonstrated using the Lagrangian model. The longitudinal dispersivity of the reactive plume can be on the order of hundreds to thousands of meters, and it may not reach its asymptotic value even after 10,000 years of travel time and 2–3 km of travel distance. [ABSTRACT FROM AUTHOR]

The geologic architecture in sedimentary reservoirs affects the behavior of density-driven flow and the dispersion of CO2-rich brine. The spatial organization and connectivity of facies types play an important role. Low-permeability facies may suppress fingering and reduce vertical spreading, but may also increase transverse mixing. This is more pronounced when geologic structures create preferential flow pathways through connected facies types. We perform high-resolution simulations of three-dimensional (3D) heterogeneous formations whose connectivity cannot be represented in two-dimensional models consistent with percolation theory. This work focuses on the importance of 3D facies-based heterogeneity and connectivity on advection-diffusion transport of dissolved CO2. Because the dissolution of CO2 and the subsequent density increase of brine are the driving force for gravitational instabilities, we model the phase behavior with the accurate cubic-plus-association equation-of-state, which accounts for the self-association of polar water molecules and the cross-association between CO2 and water. Our results elucidate how the spatial organization of facies affects the dynamics of CO2 convective mixing. Scaling relations for the evolution of a global dispersion-width provide insights that can be universally applied. The results suggest that the long-term evolution and scaling of dispersion are surprisingly similar for homogeneous and (binary and multiscale) heterogeneous porous media. [ABSTRACT FROM AUTHOR]

Summary In the method of deterministic geostatistics ( sensu Isaaks and Srivastava, 1988), highly-resolved data sets are used to compute sample spatial-bivariate statistics within a deterministic framework. The general goal is to observe what real, highly resolved, sample spatial-bivariate correlation looks like when it is well-quantified in naturally-occurring sedimentary aquifers. Furthermore, it is to understand how this correlation structure, (i.e. shape and correlation range) is related to independent and physically quantifiable attributes of the sedimentary architecture. The approach has evolved among work by Rubin (1995, 2003), Barrash and Clemo (2002), Ritzi et al. (2004, 2007, 2013), Dai et al. (2005), and Ramanathan et al. (2010). In this evolution, equations for sample statistics have been developed which allow tracking the facies types at the heads and tails of lag vectors. The goal is to observe and thereby understand how aspects of the sedimentary architecture affect the well-supported sample statistics. The approach has been used to study heterogeneity at a number of sites, representing a variety of depositional environments, with highly resolved data sets. What have we learned? We offer and support an opinion that the single most important insight derived from these studies is that the structure of spatial-bivariate correlation is essentially the cross-transition probability structure, determined by the sedimentary architecture. More than one scale of hierarchical sedimentary architecture has been represented in these studies, and a hierarchy of cross-transition probability structures was found to define the correlation structure in all cases. This insight allows decomposing contributions from different scales of the sedimentary architecture, and has led to a more fundamental understanding of mass transport processes including mechanical dispersion of solutes within aquifers, and the time-dependent retardation of reactive solutes. These processes can now be described in terms of quantifiable physical attributes of the sedimentary architecture. In addition to discussing the benefits to basic scientific understanding, we also discuss some of the practical benefits derived from this insight. [ABSTRACT FROM AUTHOR]

Understanding multi-phase fluid flow and transport processes within aquifers, candidate reservoirs for CO2 sequestration, and petroleum reservoirs requires understanding a diverse set of geologic properties of the aquifer or reservoir, over a wide range of spatial and temporal scales. We focus on multiphase flow dynamics with wetting (e.g., water) and non-wetting (e.g., gas or oil) fluids, with one invading another. This problem is of general interest in a number of fields and is illustrated here by considering the sweep efficiency of oil during a waterflood. Using a relatively fine-resolution grid throughout a relatively large domain in these simulations and probing the results with advanced scientific visualization tools (Reservoir Visualization Analysis [RVA]/ ParaView software) promote a better understanding of how smaller-scale features affect the aggregate behavior at larger scales. We studied the effects on oil-sweep efficiency of the proportion, hierarchical organization, and connectivity of high-permeability open-framework conglomerate (OFC) cross-sets within the multi-scale stratal architecture found in fluvial deposits. We analyzed oil production rate, water breakthrough time, and spatial and temporal distribution of residual oil saturation. As expected, the effective permeability of the reservoir exhibits large-scale anisotropy created by the organization of OFC cross-sets within unit bars, and the organization of unit bars within compound- bars. As a result, oil-sweep efficiency critically depends on the direction of the pressure gradient. However, contrary to expectations, the total amount of trapped oil due to the effect of capillary trapping does not depend on the magnitude of the pressure gradient within the examined range. Hence the pressure difference between production and injection wells does not affect sweep efficiency; although the spatial distribution of oil remaining in the reservoir depends on this value. Whether or not clusters of connected OFC span the domain affects only the absolute rate of oil production--not sweep efficiency. [ABSTRACT FROM AUTHOR]

This technical note presents a useful methodology for studying how the variance of hydraulic and/or reactive attributes of an aquifer are linked to the multiscaled and hierarchical sedimentary architecture of the aquifer. A new recursive equation is derived which quantitatively describes how the variance is related to sedimentary facies defined at all scales across an entire stratal hierarchy. As compared to prior published equations that emphasize differences in means among facies populations within a hierarchical level, it emphasizes differences across levels. Because of the hierarchical relationships among the terms of the equation, we find it to be useful for conducting a holistic analysis of the relative contributions to the variance arising from all facies types defined across all scales. The methodology is demonstrated using appropriate field data, and is shown to be useful in defining parsimonious classification systems. [ABSTRACT FROM AUTHOR]

• Continuous, three-dimensional redox data reveals subsurface geochemical dynamics. • High-E H conditions persist along high-permeability preferential flow paths. • Sediment lithology governs patterns of subsurface redox zonation during hot moments. The interactions between surface water-groundwater (hyporheic) exchange and sediment lithology influence oxidation–reduction (redox) conditions near river-aquifer interfaces. Redox dynamics have implications for subsurface nutrient transformations, but are difficult to assess spatially and temporally using traditional geochemical observations. This study applied continuous, three-dimensional monitoring of redox potential in a shallow riparian aquifer to assess the geochemical response to sediment lithology and hydrogeologic variables. We mapped a series of cross-sectional redox contour plots from throughout the aquifer over a storm event, which captured the dynamics of redox conditions during stage-driven mixing. Results show that redox potential increased by over 400 mV at some locations following river water infiltration during the storm, while persistent zones of high potential endured along preferential, high-conductivity flow paths. Conditions within high-conductivity (>1 cm/s) sediments took longer to recover (up to 30 days) following storms than those within finer, low-conductivity (6.9 × 10−5 cm/s) sediments (days to hours), however, attributed to less organic matter driving nutrient transformations. Dynamic surface water-groundwater interactions intermittently perturb redox conditions, but the underlying sediment lithology ultimately governs the spatial and temporal dynamics of aquifer redox conditions. These novel observations provide fundamental insights into hyporheic exchange and riparian geochemical dynamics broadly applicable to hydrogeology and biogeochemistry. [ABSTRACT FROM AUTHOR]

Highlights • Hierarchical spatial correlation models are investigated. • Inverse modeling is used to find parameters for correlation models. • Estimated correlation models provides accurate spatial correlation structure. Abstract Outcrop analogs for aquifers allow measurements of permeability with relatively high-resolution and mapping sedimentary unit types. The spatial bivariate structure of permeability is defined by aquifer architecture. The architecture is often organized into a hierarchy of unit types, and associated permeability modes, across different spatial scales. The composite covariance (or semivariogram) is a linear summation of the auto- and cross-covariances (or semivariograms) of unit types defined at smaller scales, weighted by the related proportions and transition probabilities. It is well-known that an appreciable fraction of the composite variance arises from differences in mean permeability across unit types defined at smaller scales. Previous work has shown that the transition probabilities usually define the spatial bivariate correlation structure. The composite spatial bivariate statistics for permeability (covariance or semivariogram) will not be representative unless data locations allow proper definition of the transition probabilities of the units. Quantification of the stratal architecture can be used to better interpret the transition probabilities and thereby improve a model for the sample covariance and semivariogram. In this paper, we use an inverse modeling algorithm to fit the components of the hierarchical model, written as nested functions, in developing a hierarchical spatial correlation models. Specifically, the least-squares criterion along with prior information and other weighted constraints are used as the objective function for the inverse problem, which is solved by the Gauss–Newton–Levenberg–Marquardt method. The estimated covariance and transition probability models provides accurate representation of the spatial correlation structure of permeability for field-measured data from in Española Basin, New Mexico. [ABSTRACT FROM AUTHOR]

• Uncertainty of stochastic system responses may lead to non-unique inversion results of high-dimensional parameters when adopting entropy-based monitoring network design criteria. • An ensemble monitoring network is developed to enhance the robustness of the inversion framework for heterogeneous permeability field estimation. • Study evaluates the influence of observation information in various identification scenarios on the inversion results of high-dimensional parameters. Accurately estimating high-dimensional permeability ( k ) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework. An ensemble MIMR-optimized method is developed to provide more comprehensive monitoring information and avoid missing key information due to the randomness of stochastic response datasets in entropy analysis. A numerical case of solute transport with log-Gaussian permeability fields is presented, with twelve scenarios designed by combining different measurement types and monitoring strategies. The results demonstrated that the proposed ensemble MIMR-optimized method significantly improved the k -field estimates compared to the conventional MIMR method. Additionally, high prediction accuracy in forward modeling is essential for ensuring reliable inversion results, especially for observation data with strong nonlinearity. The findings of this study enhance our understanding and management of k -field estimation in heterogeneous aquifers, contributing to the development of more robust inversion frameworks for general data assimilation tasks. [Display omitted] [ABSTRACT FROM AUTHOR]

• A cDC-GAN surrogate model is used for reactive transport modeling in hyporheic zone. • cDC-GAN is capable of effectively capturing complex, nonlinear data relationships. • The impact of aquifer heterogeneity on surrogate model performance is discussed. This study explores the application of advanced deep learning techniques, specifically conditional deep convolutional generative adversarial networks (cDC-GANs), to model and predict the complex dynamics of the hyporheic zone (HZ) driven by river stage fluctuations. The HZ is a critical transitional mixing zone between surface water and groundwater, where significant biogeochemical reactions occur. Traditional process-based reactive transport modeling methods are computationally intensive and require fine-scale parameterization. To overcome these challenges, we employ the cDC-GAN as a deep-learning-based surrogate model to predict zones of enhanced biogeochemical activity (i.e., hotspots) and reaction product concentrations in the HZ under varying bimodal sedimentary heterogeneity scenarios. The cDC-GAN model can efficiently capture nonlinear relationships between input data and output parameters without extensive parameterization, making it computationally less demanding. The model's generative capabilities allow for the creation of new data instances, enabling the exploration of diverse scenarios and interpolation between observed data points. The model uses maps of liquid saturation and solute concentrations at different time steps to generate corresponding maps of reaction rates and reaction product concentrations. Results demonstrate the model's qualitative and quantitative ability to capture complex relationships without relying on rigid assumptions about linearity or specific probability distribution functions. Notably, even amid variations in aquifer heterogeneity, the model consistently exhibits robust performance, validating its ability to adapt to dynamic geological settings. Our use of a simplified reaction scheme serves as a proof of concept for the cDC-GAN model's ability to simulate solute transport in the HZ, opening avenues for future applications including more complex reaction networks within HZ system such as nutrient cycling, organic matter degradation, redox reactions, and beyond. [ABSTRACT FROM AUTHOR]

• An aquifer structure inversion framework is developed based on the single-sample generative adversarial network. • The training speed of the aquifer structure generation with the framework is more than ten times less than that of multi-sample-based models. • Heterogeneous aquifer structures can be identified reasonably by assimilating flow and transport response data. Generating reasonable heterogeneous aquifer structures is essential for understanding the physicochemical processes controlling groundwater flow and solute transport better. The inversion process of aquifer structure identification is usually time-consuming. This study develops an integrated inversion framework, which combines the geological single-sample generative adversarial network (GeoSinGAN), the deep octave convolution dense residual network (DOCRN), and the iterative local updating ensemble smoother (ILUES), named GeoSinGAN-DOCRN-ILUES, for more efficiently generating heterogeneous aquifer structures. The performance of the integrated framework is illustrated by two synthetic contaminant experiments. We show that GeoSinGAN can generate heterogeneous aquifer structures with geostatistical characteristics similar to those of the training sample, while its training time is at least 10 times faster than that of typical approaches (e.g., multi-sample-based GAN). The octave convolution layer and multi-residual connection enable the DOCRN to map the heterogeneity structures to the state variable fields (e.g., hydraulic head, concentration distributions) while reducing the computational cost. The results show that the integrated inversion framework of GeoSinGAN and DOCRN can effectively and reasonably generate the heterogeneous aquifer structures. [ABSTRACT FROM AUTHOR]

• Hierarchical organization and connectivity of high-permeability facies types control shape of gaseous CO 2 plume and trapping mechanisms. • Facies-dependent constitutive relations and their hysteresis fundamentally affect trapping processes as well as the dynamics of CO 2 plume. • Presence of dissolved methane in the formation brine decreases the aquifer storage capacity Fundamental understanding of processes controlling geological carbon sequestration (GCS) requires flow and transport modeling in well-characterized aquifer sedimentary deposits. We use three-dimensional, heterogeneous models based on new field studies which incorporate the most important aspects of multiscale architecture in fluvial aquifers. In addition, we use the associated facies-dependent constitutive relations for both relative permeability and capillary pressure curves and their hysteresis. The novelty of this work is to evaluate how CO 2 fate and transport is controlled by (1) the spatial organization, volume proportions, and connectivity of sedimentary facies types, (2) facies-dependent constitutive relations and their hysteretic behavior, and (3) presence and/or absence of dissolved components such as methane (CH 4) in brine. Model results suggest that the amounts of snap-off and solubility trapping are enhanced by increasing the volume proportion and degree of connectivity of high-permeability facies types. Results also demonstrate that ignoring relative permeability hysteresis leads to underestimation of snap-off trapping and overestimation of solubility trapping. In contrast, neglecting capillary pressure hysteresis (i.e., non-hysteretic P c) results in overestimation of snap-off trapping and underestimation of solubility trapping. If capillary pressure is totally neglected (i.e., P c = 0), the amount of trapped CO 2 by snap-off and dissolution are, respectively, overestimated and underestimated. Competitive CO 2 and CH 4 dissolution in brine results in exsolution of CH 4 from the aqueous phase into the gaseous phase. Facies connectivity and constitutive relations are also critical for the transport of exsolved CH 4. However, disregarding dissolved CH 4 leads to overestimation of CO 2 trapping capacities. [ABSTRACT FROM AUTHOR]