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We perform a set of reactive transport simulations in three‐dimensional fracture networks to characterize the impact of geochemical reactions on flow channelization. Flow channelization, a frequently observed phenomenon in porous and fractured subsurface rock formations, results from the spatially variable hydraulic resistance offered by a geological structure. In addition to geo‐structural features such as network connectivity, geometry, and hydraulic resistance, geochemical reactions, for example, dissolution and precipitation, can dynamically inhibit or enhance flow channelization. These geochemical processes can change the fracture permeability leading to increased flow channelization, which are localized connected regions of high volumetric flow rates that are seemingly ubiquitous in the subsurface. In our simulations, fractures partially filled with quartz are gradually dissolved until quasi‐steady state conditions are obtained. We compare the flow field's initial unreacted and final dissolved states in terms of flow and transport observations. We observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelization when compared to their unreacted counterparts. However, there is substantial variability in the magnitude of these changes which implies that the channelization strongly depends on the network structure. In turn, we identify the interplay between the particular network structure and the impact of geochemical dissolution on flow channelization. The presented results indicate that geological systems that have been weathering or reactive for longer times in older landscapes are likely to have increased flow channelization compared to their equivalent but younger counterparts, which implies a time dependence on flow channelization in fractured media. Plain Language Summary: Fractures are the primary pathways for fluid flow and solute transport in Earth's subsurface. In many of these systems, fluids passing through the fractures are out of equilibrium with the resident minerals, and various geochemical reactions occur. These geochemical processes can change the resistance to flow offered by the fractures, leading to increased flow channelization, which are localized connected regions of high flow rates. However, quantification of these impacts has been limited to the computational burden of performing requisite simulations. Through a series of reactive transport simulations in fractured media, we characterize the influence of dissolution on flow channelization using observations of flow and transport properties. We compare flow and transport in the initial unreacted state and in the final dissolved state to better understand how geochemical reactions influences the flow properties of the network medium. We observe that the unreacted fracture networks provide lower resistance to flow and exhibit increased flow channelization. The results suggest that older geological systems ought to have increased flow channelization when compared to their younger counterparts. Key Points: We characterize the impact of geochemical dissolution on flow channelization in fractured media using reactive transport simulationsWe observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelizationThere is a dissolution feedback loop between primary sub‐networks and geochemical reactions on flow channelization [ABSTRACT FROM AUTHOR]

Exploratory data analysis (EDA) for functional data—data objects where observations are entire functions—is a difficult problem that has seen significant attention in recent literature. This surge in interest is motivated by the ubiquitous nature of functional data, which are prevalent in applications across fields such as meteorology, biology, medicine and engineering. Empirical probability density functions (PDFs) can be viewed as constrained functional data objects that must integrate to one and be nonnegative. They show up in contexts such as yearly income distributions, zooplankton size structure in oceanography and in connectivity patterns in the brain, among others. While PDF data are certainly common in modern research, little attention has been given to EDA specifically for PDFs. In this paper, we extend several methods for EDA on functional data for PDFs and compare them on simulated data that exhibit different types of variation, designed to mimic that seen in real‐world applications. We then use our new methods to perform EDA on the breakthrough curves observed in gas transport simulations for underground fracture networks. [ABSTRACT FROM AUTHOR]

Simulations of rock damage and gas transport following underground explosions that omit preexisting fracture networks in the subsurface cannot fully characterize the influence of geo-structural variability on gas transport. Previous studies do not consider the impact that fracture network structure and variability have on gas seepage. In this study, we develop a sequentially coupled, axi-symmetric model to look at the damage pattern and resulting gas breakthrough curves following an underground explosion given different fracture network realizations. We simulate 0.327 and 0.164 kT chemical explosives with burial depths of 100 m for 90 stochastically generated fracture networks. Gases quickly reach the surface in 30% of the higher yield simulations and 5% of the lower yield simulations. The fast breakthrough can be attributed to the formation of connected pathways between fractures to the surface. The formation of a connected damage pathway to the surface is not clearly correlated with the fracture intensity ( P 32 ) in our simulations. Breakthrough curves with slower transport are highly variable depending on the fracture network sample. The variability in the breakthrough behavior indicates that ignoring the influence of fracture networks on rock damage, which strongly influences the hydraulic properties following an underground explosion, will likely lead to a large underestimation of the uncertainty in the gas transport to the surface. This work highlights the need for incorporation of fracture networks into models for accurately predicting gas seepage following underground explosions. [ABSTRACT FROM AUTHOR]

We study the large-scale dynamics and prediction of hydrodynamic transport in random fracture networks. The flow and transport behaviour is characterized by first passage times and displacement statistics, which show heavy tails and anomalous dispersion with a strong dependence on the injection condition. The origin of these behaviours is investigated in terms of Lagrangian velocities sampled equidistantly along particle trajectories, unlike classical sampling strategies at a constant rate. The velocity series are analysed by their copula density, the joint distribution of the velocity unit scores, which reveals a simple, albeit hidden, correlation structure that can be described by a Gaussian copula. Based on this insight, we derive a Langevin equation for the evolution of equidistant particle speeds. In this framework, particle motion is quantified by a stochastic time-domain random walk, the joint density of particle position, and speed satisfies a Klein--Kramers equation. The upscaled theory quantifies particle motion in terms of the characteristic fracture length scale and the distribution of Eulerian flow velocities. That is, it is predictive in the sense that it does not require the a priori knowledge of transport attributes. The upscaled model captures non-Fickian transport features, and their dependence on the injection conditions in terms of the velocity point statistics and average fracture length. It shows that the first passage times and displacement moments are dominated by extremes occurring at the first step. The presented approach integrates the interaction of flow and structure into a predictive model for large-scale transport in random fracture networks. [ABSTRACT FROM AUTHOR]

We performed a set of numerical simulations to characterize the interplay of fracture network topology, upscaling, and mesh refinement on flow and transport properties in fractured porous media. We generated a set of generic three-dimensional discrete fracture networks at various densities, where the radii of the fractures were sampled from a truncated power-law distribution, and whose parameters were loosely based on field site characterizations. We also considered five network densities, which were defined using a dimensionless version of density based on percolation theory. Once the networks were generated, we upscaled them into a single continuum model using the upscaled discrete fracture matrix model presented by Sweeney et al. (2019). We considered steady, isothermal pressure-driven flow through each domain and then simulated conservative, decaying, and adsorbing tracers using a pulse injection into the domain. For each simulation, we calculated the effective permeability and solute breakthrough curves as quantities of interest to compare between network realizations. We found that selecting a mesh resolution such that the global topology of the upscaled mesh matches the fracture network is essential. If the upscaled mesh has a connected pathway of fracture (higher permeability) cells but the fracture network does not, then the estimates for effective permeability and solute breakthrough will be incorrect. False connections cannot be eliminated entirely, but they can be managed by choosing appropriate mesh resolution and refinement for a given network. Adopting octree meshing to obtain sufficient levels of refinement leads to fewer computational cells (up to a 90% reduction in overall cell count) when compared to using a uniform resolution grid and can result in a more accurate continuum representation of the true fracture network. [ABSTRACT FROM AUTHOR]

The inherent uncertainty of subsurface fracture characteristics requires an ensemble-based approach where multiple network realizations are generated to represent a single physical system. However, the computational cost of these simulations is often prohibitive for carrying out an adequate number of simulations to obtain stable statistics for many quantities of interest, including the first passage time distribution (FPTD) of particles passing through the system. We characterize how variability induced by stochastic representations of subsurface fracture networks propagates into the FPTD. We simulate flow and transport on a large ensemble of three-dimensional fracture networks, observe the quantiles of the first passage time distributions, and characterize the network structure using coarse graph-based representations. Through analysis of the first passage times and graphs, we identify key geostructural features which explain variation in the FPTD. These features integrate hydrological fracture properties (permeability) with topological attributes. Using these features, we fit both parametric and nonparametric regression models to predict FPTD with uncertainty, compare the relative performance of each model in terms of error and coverage, and discuss possible model extensions. Models are validated using a held-out set of networks independently generated under different parameter settings. These nonparametric regression models can flexibly account for nonlinear relationships while allowing prediction intervals to adjust accurately depending on input feature values. [ABSTRACT FROM AUTHOR]

We model flow and transport in fracture networks with varying degrees of fracture‐to‐fracture aperture heterogeneity and network intensity to show how changes in these properties can cause the emergence of anomalous flow and transport behavior. If fracture‐to‐fracture aperture heterogeneity is increased in sparse networks, velocity fluctuations inhibit high flow rates and solute transport can be delayed. Surprisingly, transport can be slowed even in cases where hydraulic aperture is monotonically increased. As the intensity of the networks is increased, more connected pathways allow for particles to bypass these effects. There exists transition behavior where with relatively few connected pathways in a network, first arrival times of particles are not heavily affected by fracture‐to‐fracture aperture heterogeneity, but the scaling behavior of the tails is strongly influenced. These results reinforce the importance of considering multi‐scale effects in fractured systems and can inform flow and transport processes in both natural and engineered fractured systems. Plain Language Summary: Fractured rocks are important to study to understand subsurface geology, hydrology, and engineered systems. Individual fractures often form connected networks with other fractures, which complicates the prediction of flow and transport behavior. In this work, we study how changes in fracture network properties such as the variability of fracture apertures and the number of fractures in a network, affect flow and transport observables, such as the amount of actively flowing network structure, as well as the arrival times of solutes. Our results point to rich behavior where there is a close link between how much variability exists between individual fracture apertures, the network intensity, and the behavior of the solute transport. When fracture networks have relatively few fractures, increases in the magnitude of the fracture apertures and the variability between individual apertures can cause unusual changes in the flow and transport, causing delays in solute transport times. However, these effects are gradually lost as the number of fractures in the network is increased. Our results can inform both natural hydrological processes, as well as engineered systems, such as enhanced geothermal systems and hydraulic fracturing because these systems often create high aperture fractures that connect to natural fractures with lower aperture. Key Points: We investigate the influence of multi‐scale heterogeneity in fracture networks on flow and transport using discrete fracture network modelsHigh aperture heterogeneity across a sparse fracture network results in counter‐intuitive flow and transport behaviorEffects of fracture‐to‐fracture aperture heterogeneity are limited at high network intensities [ABSTRACT FROM AUTHOR]

Fractured reservoirs are complex and multi‐scale systems composed of matrix and fractures. Accordingly, modeling flow in such geological media has been a great challenge. In this study, we investigated the effect of scale as well as matrix and fracture network characteristics on the effective permeability (keff) in matrix‐fracture systems under fully saturated conditions. We generated fracture networks, embedded within a matrix of permeability of 10−18 m2, with fracture lengths followed a truncated power‐law distribution with exponent α = 1.5, 2.0, and 2.5. We set fracture permeability equal to 10−16, 10−14, and 10−12 m2 and numerically simulated fluid flow to determine the keff at six fracture densities for 36 fractured reservoirs. Results showed that the effect of α and scale on the keff became more significant as the contrast between matrix and fracture permeabilities increased. We also fit the percolation‐based effective‐medium approximation (P‐EMA) to the simulations and optimized its two parameters critical fracture density and scaling exponent. Results exhibited that both P‐EMA model parameters were scale‐dependent. Through linear regression analysis, we found that the critical fracture density and scaling exponent were highly correlated to other matrix‐fracture system properties and proposed two regression‐based models evaluated using a new six sets of simulations. Comparing the estimated keff values with the simulated ones demonstrated the reliability and predictability of the P‐EMA. The matrix‐fracture systems studied here were finite in size. We also showed that one may extend results to infinitely large reservoirs using the P‐EMA framework. Key Points: Scale‐dependent effective permeability in matrix‐fracture systems is investigated using numerical simulations and theoretical modelingCritical fracture density and scaling exponent are linked to reservoir properties and its size, and results are extended to large systemsTheoretical estimations of effective permeability in six unseen fractured reservoirs agree with numerical simulations well [ABSTRACT FROM AUTHOR]

Understanding whether fractures and faults impact the CO2 migration through the overburden is critical in the evaluation and monitoring of CO2 geological storage sites. We present a numerical model and workflow to describe the hydraulic behaviour of a fault located in the shallow part of the overburden. This helps to evaluate the sealing potential of the system in case of unwanted CO2 migration toward the surface and to design an efficient monitoring plan. The model configuration is representative of several experiments performed at real sites under quite shallow conditions (50–500 m). The model results, applied to a selected fault outcropping in the Apennines (Italy), show that most of the gas migrates through the high permeable footwall damage zone. A significant amount of gas then dissolves into the water, emphasizing the importance of accurate modelling to assess the hazard of CO2 leakage into near-surface aquifers or to the surface. The occurrence of pressure buildup close to the fault core points out that detailed modelling of the migration conditions is required to predict gas path through a fault zone. Thematic collection: This article is part of the Fault and top seals 2022 collection available at: https://www.lyellcollection.org/topic/collections/fault-and-top-seals-2022 [ABSTRACT FROM AUTHOR]

Understanding mechanistic causes of non‐Fickian transport in fractured media is important for many hydrogeologic processes and subsurface applications. This study elucidates the effects of dead‐end fractures on non‐Fickian transport in three‐dimensional (3D) fracture networks. Although dead‐end fractures have been identified as low‐velocity regions that could delay solute transport, the direct relation between dead‐end fractures and non‐Fickian transport has been elusive. We systematically generate a large number of 3D discrete fracture networks with different fracture length distributions and fracture densities. We then identify dead‐end fractures using a novel graph‐based method. The effect of dead‐end fractures on solute residence time maximizes at the critical fracture density of the percolation threshold, leading to strong late‐time tailing. As fracture density increases beyond the percolation threshold, the network connectivity increases, and dead‐end fractures diminish. Consequently, the increase in network connectivity leads to a reduction in the degree of late‐time tailing. We also show that dead‐end fractures can inform about main transport paths, such as the mean tortuosity of particle trajectories. This study advances our mechanistic understanding of solute transport in 3D fracture networks. Plain Language Summary: A critical phenomenon often observed in fractured rocks is an anomalously slow decay of solute concentration, which is often called solute tailing. Long retention of solute contaminants poses severe threats to human health, and predicting solute tailing is critical for remediation purposes. However, solute tailing is challenging to predict, and understanding the mechanisms inducing solute tailing is important for improving our predictive capability. Recent studies showed that dead‐end fractures form slow‐flow regions that could cause solute tailing. However, no study has explicitly quantified the effects of dead‐end fractures on solute tailing. By simulating flow and transport in three‐dimensional fracture networks over more than 200 realizations, we find that the relationship between fracture density and dead‐end fracture formation is non‐monotonic. Specifically, we observe that there exists a critical level of fracture density where the presence of dead‐end fractures is maximized. At the optimum level, solute particles' residence time in dead‐end fractures significantly increases, leading to strong solute tailing. As fracture density increases beyond the optimum, dead‐end fractures diminish due to the improved network connectivity, reducing solute tailing. This study improves our mechanistic understanding of how dead‐end fractures control solute tailing in fracture networks. Key Points: A new graph‐based measure that identifies dead‐end fractures is proposedLate‐time solute tailing is maximized at the percolation thresholdThe density of dead‐end fractures is a good predictor of non‐Fickian transport [ABSTRACT FROM AUTHOR]

While several studies have linked network and in-fracture scale properties to conservative transport behavior in subsurface fractured media, studies on reactive transport cases remain relatively underdeveloped. In this study, we explore the behavior of an irreversible kinetic reaction during the interaction of two solute plumes, one consisting of species A and the other species B. When the plumes converge, these species react kinetically to form a new species C via A + B → k C . This reactive system is studied using a three-dimensional discrete fracture network (DFN) model coupled with reactive Lagrangian particle tracking. We find that the interplay of network topology and chemical properties of the reactive solutes controls reactive transport processes. The network topology drives species A and B together, and the chemical properties dictate whether and how quickly a reaction occurs. Results demonstrate that reactions are most likely to occur in high-velocity fractures that make up the network backbone. The interplay between species' chemical properties and transport is characterized by a non-dimensional Damköhler (Da) number. We show that the spatial distribution of reactions is sensitive to Da, which subsequently influences late-time tailing behavior in outlet breakthrough time distributions. The results of this study provide initial insights into how an irreversible reaction occurs during transport in a fracture network, using a methodology that can be applied to study reactive transport in a wide range of fractured media environments and contexts. Article Highlights: Discrete Fracture Network models are used to study reactive transport behavior. We consider the irreversible chemical reaction A + B → C Reactions primarily occur in the network backbone and reaction locations are sensitive to chemical properties. [ABSTRACT FROM AUTHOR]

Field measurements of apparent geochemical weathering reaction rates in subsurface fractured porous media are known to deviate from laboratory measurements by multiple orders of magnitude. To date, there is no geologically based explanation for this discrepancy that can be used to predict reaction rates in field systems. Proposed correction factors are typically based on ad hoc characterizations related to geochemical kinetic models. Through a series of high‐fidelity reactive transport simulations of mineral dissolution within explicit 3D discrete fracture networks, we are able to link the geo‐structural attributes with reactive transport observations. We develop a correction factor to linear transition state theory for the prediction of the apparent dissolution rate based on measurable geological properties. The modified rate law shows excellent agreement with numerical simulations, indicating that geological structure could be a primary reason for the discrepancy between laboratory and field observations of apparent dissolution rates in fractured media. Plain Language Summary: Fractures are the principal conduits for fluid flow through low permeability rock in the Earth's subsurface. In many of these systems, fluids passing through the fractures are out of equilibrium with the resident minerals, and various reactions, such as dissolution and precipitation, occur. These geochemical processes change the fracture permeability and drive fracture propagation, thereby dynamically changing flows. Field measurements of apparent geochemical weathering reaction rates are lower than laboratory measurements by multiple orders of magnitude, which makes predictions of geochemical reaction rates highly uncertain. These slow apparent dissolution rates are particularly pronounced in fracture networks where geo‐structural attributes, for example, the network connectivity and fracture geometry, determine the flow field structure and dictate transport. Through a series of high‐fidelity reactive transport simulations of mineral dissolution in fractured media, we uncovered a new link between the geo‐structural attributes of the underlying fracture network with reactive transport observations. Guided by this information, we develop a correction factor to linear transition state theory to predict the apparent dissolution rate that is based on these geological attributes. The excellent agreement of the proposed model with our numerical simulations indicates that geological structure could be one of the reasons for the commonly observed discrepancy. Key Points: Observations of apparent reaction rates in fractured media are orders of magnitude lower than those measured in laboratory conditionsReactive transport simulations are used to characterize the influence of 3D fracture network structure on apparent dissolution ratesA geo‐structurally based modification to linear transition state theory for the prediction of the apparent dissolution rate is presented [ABSTRACT FROM AUTHOR]

Fluid flow in fractures has the potential to drastically change the economic, environmental, and safety risks associated with a subsurface operation. This work focuses on the analysis of experimental shear fracturing and subsequent shear displacement of a shale specimen that contains multiple pre-existing natural fractures. A triaxial direct shear apparatus with concurrent µCT imaging allowed continuous monitoring of sample permeability and changes in fracture geometry during fracture creation, displacement, and changes in stress. Steady-state, low Reynolds number fluid flow simulations were used to determine the changes of each individual fracture's hydraulic aperture due to displacement and effective stress. The development of anisotropy in the hydraulic aperture of a generated shear fracture was determined for shear displacements of 0.38, 0.74, and 2.04 mm. At high confining stress (30 MPa), shear displacement significantly reduced the hydraulic aperture of the shear fracture to below flow detectable limits and X-ray imaging resolution (23 × 23 × 23 µm3). The shear generated fracture was found to be more sensitive to changes in effective stress compared to the pre-existing natural fractures. Existing models derived from fluid flow equations accounting for stochastic fracture roughness were able to partially fit the simulated flow properties of the pre-existing fractures, but no model was found to fit the flow properties of the newly formed shear fracture. These results are the first report of simultaneous changes in the geometry and flow properties of surrounding, parallel natural fractures as a shear fracture is generated with varying stress and shear displacement conditions. Article Highlights: Flow anisotropy in a shear generated fracture varies based on shear displacement. Fracture flow channelization increases with shear displacement. Flow in a newly generated shear fracture has a larger response to changes in stress than pre-existing natural fractures. [ABSTRACT FROM AUTHOR]

We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconventionals, namely the lack of data (i.e., the time-frame for which the wells have been producing), and the significant computational expense of high-fidelity modeling. We do this by applying the machine-learning paradigm of transfer learning, where we combine fast, but less accurate reduced-order models with slow, but accurate high-fidelity models. We use the Patzek model (Proc Natl Acad Sci 11:19731–19736, https://doi.org/10.1073/pnas.1313380110, 2013) as the reduced-order model to generate synthetic production data and supplement this data with synthetic production data obtained from high-fidelity discrete fracture network simulations of the site of interest. Our results demonstrate that training with low-fidelity models is not sufficient for accurate forecasting, but transfer learning is able to augment the knowledge and perform well once trained with the small set of results from the high-fidelity model. Such a physics-informed machine-learning (PIML) workflow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. [ABSTRACT FROM AUTHOR]

We present a topology-based method for mesh-partitioning in three-dimensional discrete fracture network (DFN) simulations that takes advantage of the intrinsic multi-level nature of a DFN. DFN models are used to simulate flow and transport through low-permeability fractured media in the subsurface by explicitly representing fractures as discrete entities. The governing equations for flow and transport are numerically integrated on computational meshes generated on the interconnected fracture networks. Modern high-fidelity DFN simulations require high-performance computing on multiple processors where performance and scalability depends partially on obtaining a high-quality partition of the mesh to balance work-loads and minimize communication across all processors. The discrete structure of a DFN naturally lends itself to various graph representations, which can be thought of as coarse-scale representations of the computational mesh. Using this concept, we develop two applications of the multilevel graph partitioning algorithm to partition the mesh of a DFN. In the first, we project a partition of the graph based on the DFN topology onto the mesh of the DFN and in the second, this DFN-based projection is used as the initial condition for further partitioning refinement of the mesh. We compare the performance of these methods with standard multi-level graph partitioning using graph-based metrics (cut, imbalance, partitioning time), computational-based metrics (FLOPS, iterations, solver time), and total run time. The DFN-based and the mesh-based partitioning methods are comparable in terms of the graph-based metrics, but the time required to obtain the partition is several orders of magnitude faster using the DFN-based partitions. The computation-based metrics show comparable performance between both methods so, in combination, the DFN-based partitions are several orders of magnitude faster than the mesh-based partition. Moreover, the method which uses the DFN-partition solution as the initial condition of the mesh partition provided cut and imbalance values that were close to the mesh-based partition but in a fraction of the time. In turn, this hybrid method outperformed both of the other methods in terms of the total run time. [ABSTRACT FROM AUTHOR]

We incorporate observations of real fracture aperture variability observed in laboratory experiments into an ensemble of three‐dimensional discrete fracture network (DFN) simulations to characterize how variations of this micro‐scale feature can influence flow and transport behavior at the network scale. A shear fracture is created within a Marcellus shale sample, and the fracture aperture is measured using a triaxial direct‐shear device coupled with real‐time X‐ray imaging at in‐situ stress conditions. We construct an ensemble of fracture networks based on natural fractures in Marcellus shale and project regions of the experimental aperture field onto each fracture in the networks. Our calculations demonstrate that the degree of flow channelization, a network‐scale flow field structure, is dramatically increased by local changes in the aperture field that in turn affects flow and transport properties. Plain Language Summary: Fluid flow and solute transport through subsurface fractured media are inherently multi‐scale phenomena. Individual fractures connect to form complicated networks where relevant length scales span multiple orders of magnitude. In this letter, we address a long‐standing question in this research area regarding the relative impact of micro‐scale heterogeneity of fracture roughness, that is, internal aperture variability, on flow and transport at the network macro‐scale. To do so, we developed a methodology to incorporate real apertures obtained from in‐situ observations of dynamically fractured laboratory specimens into three‐dimensional discrete fracture network simulations for the first time. This methodology allows us to characterize how variations in fracture aperture can lead to increased flow channelization at the network scale, a phenomenon that we refer to as scale‐bridging. Our results show that this natural aperture variability not only modifies the local flow field within a single fracture but can re‐structure the global flow field of the entire network. In turn, the distribution of solute transport behavior is drastically modified, the networks' active surface area is drastically decreased, and the degree of flow channelization is markedly increased compared to reference networks that use equivalent smooth fractures. Key Points: We incorporate X‐ray imaged fracture apertures, obtained at high‐stress in‐situ conditions, into 3D DFN flow and transport simulationsWe observe the effects of incorporating real aperture data for fractures on network‐scale flow and transport properties for the first timeInclusion of aperture variability leads to scale‐bridging, where this small‐scale feature reorganizes the flow field at the network scale [ABSTRACT FROM AUTHOR]

We incorporate observations of real fracture aperture variability observed in laboratory experiments into an ensemble of three‐dimensional discrete fracture network (DFN) simulations to characterize how variations of this micro‐scale feature can influence flow and transport behavior at the network scale. A shear fracture is created within a Marcellus shale sample, and the fracture aperture is measured using a triaxial direct‐shear device coupled with real‐time X‐ray imaging at in‐situ stress conditions. We construct an ensemble of fracture networks based on natural fractures in Marcellus shale and project regions of the experimental aperture field onto each fracture in the networks. Our calculations demonstrate that the degree of flow channelization, a network‐scale flow field structure, is dramatically increased by local changes in the aperture field that in turn affects flow and transport properties. Plain Language Summary: Fluid flow and solute transport through subsurface fractured media are inherently multi‐scale phenomena. Individual fractures connect to form complicated networks where relevant length scales span multiple orders of magnitude. In this letter, we address a long‐standing question in this research area regarding the relative impact of micro‐scale heterogeneity of fracture roughness, that is, internal aperture variability, on flow and transport at the network macro‐scale. To do so, we developed a methodology to incorporate real apertures obtained from in‐situ observations of dynamically fractured laboratory specimens into three‐dimensional discrete fracture network simulations for the first time. This methodology allows us to characterize how variations in fracture aperture can lead to increased flow channelization at the network scale, a phenomenon that we refer to as scale‐bridging. Our results show that this natural aperture variability not only modifies the local flow field within a single fracture but can re‐structure the global flow field of the entire network. In turn, the distribution of solute transport behavior is drastically modified, the networks' active surface area is drastically decreased, and the degree of flow channelization is markedly increased compared to reference networks that use equivalent smooth fractures. Key Points: We incorporate X‐ray imaged fracture apertures, obtained at high‐stress in‐situ conditions, into 3D DFN flow and transport simulationsWe observe the effects of incorporating real aperture data for fractures on network‐scale flow and transport properties for the first timeInclusion of aperture variability leads to scale‐bridging, where this small‐scale feature reorganizes the flow field at the network scale [ABSTRACT FROM AUTHOR]

Objective: To examine the variety of fruits and vegetables lower income households in the USA can buy while meeting Federal dietary recommendations at different levels of expenditure. Design: Simulation techniques were used to create 3000 market baskets of fruits and vegetables. All baskets contained enough food for a four-person household to meet dietary recommendations for fruits and vegetables over 1 week. Each basket's retail value was estimated along with the ability of a representative household to afford each basket with different levels of expenditure. Setting: We used data from the US Department of Agriculture's (USDA) Fruit and Vegetable Prices data product which reports a US household's costs to buy each of 157 different fruit and vegetable products per edible cup equivalent. Participants: We consider the situation facing a lower income household that receives maximum benefits through the Supplemental Nutrition Assistance Program (SNAP). These benefits are enough for the household to obtain a nutritious and palatable diet without spending any of its own money on food if it approximately follows USDA's Thrifty Food Plan. Results: Households receiving maximum SNAP benefits can buy a sufficient variety and quantity of fruits and vegetables if they allocate about 40 % of those benefits to these two food groups. However, if households spend less than that amount, the variety of products they can buy while still satisfying recommendations drops off quickly. Conclusion: Households that move fruits and vegetables to the centre of their budgets can better afford to meet Federal dietary guidelines. [ABSTRACT FROM AUTHOR]

The distribution of fuels is recognised as a key driver of wildland fire behaviour. However, our understanding of how fuel density heterogeneity affects fire behaviour is limited because of the challenges associated with experiments that isolate fuel heterogeneity from other factors. Advances in fire behaviour modelling and computational resources provide a means to explore fire behaviour responses to fuel heterogeneity. Using an ensemble approach to simulate fire behaviour in a coupled fire–atmosphere model, we systematically tested how fuel density fidelity and heterogeneity shape effective wind characteristics that ultimately affect fire behaviour. Results showed that with increased fuel density fidelity and heterogeneity, fire spread and area burned decreased owing to a combination of fuel discontinuities and increased fine-scale turbulent wind structures that blocked forward fire spread. However, at large characteristic length scales of spatial fuel density, the fire spread and area burned increased because local fuel discontinuity decreased, and wind entrainment into the forest canopy maintained near-surface wind speeds that drove forward fire spread. These results demonstrate the importance of incorporating high-resolution fuel fidelity and heterogeneity information to capture effective wind conditions that improve fire behaviour forecasts. Ensembles of physics-based fire model simulations demonstrate that the fidelity and spatial heterogeneity of fuel influenced simulated fire behaviour. Increased variance of fuel density resulted in decreased fire behaviour metrics compared with a homogenous fuel representation. However, large forest gaps overcame the effect of increased variance of fuel density as fire behaviour metrics rebounded. [ABSTRACT FROM AUTHOR]

Objective: To examine the variety of fruits and vegetables lower income households in the USA can buy while meeting Federal dietary recommendations at different levels of expenditure.Design: Simulation techniques were used to create 3000 market baskets of fruits and vegetables. All baskets contained enough food for a four-person household to meet dietary recommendations for fruits and vegetables over 1 week. Each basket's retail value was estimated along with the ability of a representative household to afford each basket with different levels of expenditure.Setting: We used data from the US Department of Agriculture's (USDA) Fruit and Vegetable Prices data product which reports a US household's costs to buy each of 157 different fruit and vegetable products per edible cup equivalent.Participants: We consider the situation facing a lower income household that receives maximum benefits through the Supplemental Nutrition Assistance Program (SNAP). These benefits are enough for the household to obtain a nutritious and palatable diet without spending any of its own money on food if it approximately follows USDA's Thrifty Food Plan.Results: Households receiving maximum SNAP benefits can buy a sufficient variety and quantity of fruits and vegetables if they allocate about 40 % of those benefits to these two food groups. However, if households spend less than that amount, the variety of products they can buy while still satisfying recommendations drops off quickly.Conclusion: Households that move fruits and vegetables to the centre of their budgets can better afford to meet Federal dietary guidelines. [ABSTRACT FROM AUTHOR]

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. Key Points: Tracer injection mode has strong impact on transport in 3‐D DFNs across all levels of aperture heterogeneityInitial Lagrangian velocity distribution determines the late‐time power‐law scaling of a breakthrough curveA Bernoulli CTRW model effectively captures anomalous transport in 3‐D DFNs [ABSTRACT FROM AUTHOR]

Flow channelization is a commonly observed phenomenon in fractured subsurface media where the flow of fluids is restricted primarily to highly transmissive fracture networks surrounded by a low‐permeability rock matrix. The multiscale structural heterogeneity of these networks results in multiscale flow channelization where preferential flow paths form at length scales ranging from the entire system down to the subfracture size. We present an analysis of how one of the largest scales in fractured media, the network density, influences the degree of flow channeling that occurs using an ensemble of semigeneric three‐dimensional discrete fracture network (DFN) simulations. We construct 10 DFNs, whose fracture lengths follow a power law distribution, at four densities for a total of 40 networks. We characterize their structure in terms of the network topology and geometry. Eulerian and Lagrangian observations of the steady‐state flow fields obtained within the networks are used to quantify the degree of flow channelization at the network scale. We introduce a measure for the importance‐ranking/hierarchy of different flow paths in the network using graph‐based analysis of Lagrangian transport by which the degree of flow channeling between networks is compared. These flow observations are then linked to the structural properties of the networks. In general, network‐scale flow channeling decreases as the network density increases. However, at low densities, there is more uniform flow within the entire connected network than in high‐density networks. We also demonstrate how standard transport observables can be used to infer the degree of flow channelization occurring within a fracture network. [ABSTRACT FROM AUTHOR]

The phenomenon of flow-channeling, or the existence of preferential pathways for flow in fracture networks, is well known. Identification of the channels ("backbone") allows for system reduction and computational efficiency in simulation of flow and transport through fracture networks. However, the purpose of machine learning techniques for backbone identification in fractured media is two-pronged system reduction for computational efficiency in simulation of flow and transport as well as physical insight into the phenomenon of flow channeling. The most critical aspect of this problem is the need to have a truly "physics-informed" technique that respects the constraint of connectivity. We present a method that views a network as a union of connected paths with each path comprising a sequence of fractures. Thus, the fundamental unit of selection becomes a sequence of fractures, classified based on attributes that characterize the sequence. In summary, this method represents a parametrization of the sample space that ensures every selected sample sub-network (which is the union of all selected sequences of fractures) always respects the constraint of connectivity, demonstrating that it is a truly physics-informed method. The algorithm has a user-defined parameter which allows control of the backbone size when using the random forest or logistic regression classifier. Even when the backbones are less than 30% in size (leading to computational savings), the backbones still capture the behavior of the breakthrough curve of the full network. Moreover, there is no need to check for path connectedness in the backbones unlike previous methods since the backbones are guaranteed to be connected. [ABSTRACT FROM AUTHOR]

Multiphase flow is ubiquitous in subsurface energy applications and natural processes, such as oil recovery, CO2 sequestration, and water flow in soils. Despite its importance, we still lack a thorough understanding of the coupling of multiphase flow and reaction of transported fluids with the confining media, including rock dissolution and mineral precipitation. Through the use of geomaterial microfluidic flow experiments and high‐performance computer simulations, we identify key pore‐scale mechanisms that control this coupling. We compare the reactivity of fractured limestone with CO2‐saturated brine (single phase) and a mixture of supercritical (sc) CO2 and CO2‐saturated brine (multiphase). We find that the presence of scCO2 bubbles significantly changes both the flow dynamics and the resulting reaction patterns from a single‐phase system, spatially homogenizing the rock dissolution. In addition, bubbles redirect oversaturated fluid into low‐velocity regions, thereby enhancing carbonate precipitation occurs. Plain Language Summary: The impact of pore‐scale multiphase flow on fluid‐solid reactions is poorly understood because direct observations of reactive multiphase fluids in real rock materials are not widely available and the necessary computing is intractable. Using high‐pressure/temperature geomaterial microfluidic experiments complemented by high‐performance computer direct numerical simulation of multiphase flow in those geometries, we elucidate the pore‐scale mechanisms that lead to homogenization of rock dissolution and enhancement of mineral precipitation. This study contributes to our ability to predict soil weathering and to optimize CO2 sequestration and hydrocarbon extraction. Key Points: Multiphase flow leads to homogenization of rock dissolution and enhancement of mineral precipitationThe identified pore‐scale mechanisms have fundamental implications for soil weathering and subsurface applicationsThe results provide a foundation for engineering design and predictive tools [ABSTRACT FROM AUTHOR]

We introduce an innovative ensemble analysis framework for organizing, searching, and comparing results produced by hundreds of physical simulations. Our web-based approach is built on standard technologies, utilizes a scalable and modular design, and is suitable for displaying the results of in situ analysis and extreme-scale simulations. [ABSTRACT FROM AUTHOR]

We develop a computational geometry-based upscaling approach to accurately capture the dynamic processes occurring within and between subsurface fracture networks and the surrounding porous matrix named the Upscaled Discrete Fracture Matrix model (UDFM). Fracture attributes (orientations and apertures) are upscaled and combined with matrix attributes (permeability and porosity) into properties of an unstructured spatially variable Delaunay tetrahedral continuum mesh. The resolution of the mesh depends on proximity to the fractures to preserve the geometric and topological integrity of the underlying fracture network as well as increasing the accuracy of gradients in the dynamics between the fractures and matrix. The UDFM Delaunay mesh and its dual Voronoi mesh can be used in existing multiphysics simulators for flow, solute/heat mass transport, and geomechanics, thereby eliminating the need for the additional development of numerical methods to couple processes in fracture/matrix systems. The model is verified by comparing flow, transport, and coupled heat-mass flow simulations on the provided meshes against analytical and numerical benchmarks. We also provide an additional example to demonstrate the applicability of the UDFM to complex heteregenous fractured media. Overall, the UDFM is accurate in all of the cases considered here and presents an attractive alternative to other modeling strategies that require novel numerical methods or multi-dimensional meshes. [ABSTRACT FROM AUTHOR]

We present a set of multiphase flow simulations where supercritical CO 2 (scCO 2 ) displaces water at hydrostatic conditions within three-dimensional discrete fracture networks that represent paths for potential leakage through caprock above CO 2 storage reservoirs. The simulations are performed to characterize and compare the relative impact of hydraulic and structural heterogeneity in fractured media on the initial movement of scCO 2 through these caprock formations. In one scenario, intrinsic fracture permeabilities are varied stochastically within a fixed network structure. In another scenario, we generate multiple independent, identically distributed network realizations with varying fracture network densities to explore a wide range of geometric and topological configurations. Analysis of the simulations indicates that network structure, specifically connectivity and the presence of hanging fractures, plays a larger role in controlling the displacement of water by scCO 2 than variations in local hydraulic properties. We identify active surface area of the network as a single-phase feature that could provide a lower bound on the percentage of the network surface area reached by scCO 2 . [ABSTRACT FROM AUTHOR]

We develop a theoretical model for power law tailing behavior of transport in fractured rock based on the relative dominance of the decay rate of the advective travel time distribution, modeled using a Pareto distribution (with tail decaying as ∼ time−(1+α)), versus matrix diffusion, modeled using a Lévy distribution. The theory predicts that when the advective travel time distribution decays sufficiently slowly (α<1), the late‐time decay rate of the breakthrough curve is −(1+α/2) rather than the classical −3/2. However, if α>1, the −3/2 decay rate is recovered. For weak matrix diffusion or short advective first breakthrough times, we identify an early‐time regime where the breakthrough curve follows the Pareto distribution, before transitioning to the late‐time decay rate. The theoretical predictions are validated against particle tracking simulations in the three‐dimensional discrete fracture network simulator dfnWorks, where matrix diffusion is incorporated using a time domain random walk. Key Points: A theoretical model is developed for power law tailing of breakthrough curves influenced by matrix diffusion and heterogeneous advectionA threshold decay rate for the advective travel time distribution is identified,below which matrix diffusion produces tail decay rates >−3/2Matrix diffusion is implemented in high‐fidelity three‐dimensioal discrete fracture network simulations to confirm theoretical predictions [ABSTRACT FROM AUTHOR]

Discrete fracture networks (DFN) are often used to model flow and transport in fractured porous media. The accurate resolution of flow and transport behavior on a large DFN involving thousands of fractures is computationally expensive. This makes uncertainty quantification studies of quantities of interest such as travel time through the network computationally intractable, since hundreds to thousands of runs of the DFN model are required to get bounds on the uncertainty of the predictions. Prior works on the subject demonstrated that the complexity of a DFN could be reduced by considering a sub-network of it (often termed a "backbone" sub-network), one whose flow and transport properties were then shown to be similar to that of the full network. The technique is tantamount to partitioning the complete set of fractures of a network into two disjoint sets, one of which is the backbone sub-network while the other its complement. It is in this context that we present a system-reduction technique for DFNs using supervised machine learning via a Random Forest Classifier that selects a backbone sub-network from the full set of fractures. The in-sample errors (in terms of precision and recall scores) of the trained classifier are found to be very accurate indicators of the out-of-sample errors, thus exhibiting that the classifier generalizes well to test data. Moreover, this system-reduction technique yields sub-networks as small as 12% of the full DFN that still recover transport characteristics of the full network such as the peak dosage and tailing behavior for late times. Most importantly, the sub-networks remain connected, and their size can be controlled by a single dimensionless parameter. Furthermore, measures of KL-divergence and KS-statistic for the breakthrough curves of the sub-networks with respect to the full network show physically realistic trends in that the measures decrease monotonically as the size of the sub-networks increase. The computational efficiency gained by this technique depends on the size of the sub-network, but large reductions in computational time can be expected for small sub-networks, yielding as much as 90% computational savings for sub-networks that are as small as 10-12% of the full network. [ABSTRACT FROM AUTHOR]

We propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transport in fractured media. DFN models are used to simulate flow and transport in subsurface fractured rock with low-permeability. One method to alleviate the heavy computational overhead associated with these simulations is to reduce the size of the DFN using a graph representation of it to identify the primary flow sub-network and only simulate flow and transport thereon. The first of these methods used unweighted graphs constructed solely on DFN topology and could be used for accurate predictions of first-passage times. However, these techniques perform poorly when predicting later stages of the mass breakthrough. We utilize a weighted-graph representation of the DFN where edge weights are based on hydrological parameters in the DFN that allows us to exploit the kinematic quantities derivable a posteriori from the flow solution obtained on the graph representation of the DFN to perform system reduction and predict the later stages of the breakthrough curve with high fidelity. We also propose and demonstrate the use of an adaptive pruning algorithm with error control that produces a pruned DFN sub-network whose predicted mass breakthrough agrees with the original DFN within a user-specified tolerance. The method allows for the level of accuracy to be a user-controlled parameter. [ABSTRACT FROM AUTHOR]

We present a graph-based method to identify primary flow and transport subnetworks in three-dimensional discrete fracture networks (DFNs). The structure of a DFN lends itself to the use of graphs as a coarse-scale representation that retains the multiscale nature of flow and transport through fracture media. We develop a bipartite graph representation that integrates fracture network topology, fracture geometry, and hydraulic properties. We show that the two most common graph-representations of DFNs, vertices representing intersections and vertices representing fractures, are projections of this bipartite graph thereby providing a generalization of previous DFN-graph frameworks. The primary subnetworks in each DFN are identified by running a heuristic algorithm that determines the edge-disjoint shortest paths through the graph which correspond to the regions where the fastest transport occurs. The method does not have any user-defined parameters and terminates in a finite number of steps. The quality of the method is demonstrated by comparing transport simulations on the identified primary subnetwork and full network, which are in good agreement for early and middle times. These estimates of the first passage times can be achieved with close to an order of magnitude reduction of computational expense using the proposed method. [ABSTRACT FROM AUTHOR]

Structural and topological information play a key role in modeling flow and transport through fractured rock in the subsurface. Discrete fracture network (DFN) computational suites such as DFNWORKS (Hyman et al. Comput. Geosci. 84, 10-19 2015) are designed to simulate flow and transport in such porous media. Flow and transport calculations reveal that a small backbone of fractures exists, where most flow and transport occurs. Restricting the flowing fracture network to this backbone provides a significant reduction in the network’s effective size. However, the particle-tracking simulations needed to determine this reduction are computationally intensive. Such methods may be impractical for large systems or for robust uncertainty quantification of fracture networks, where thousands of forward simulations are needed to bound system behavior. In this paper, we develop an alternative network reduction approach to characterizing transport in DFNs, by combining graph theoretical and machine learning methods. We consider a graph representation where nodes signify fractures and edges denote their intersections. Using random forest and support vector machines, we rapidly identify a subnetwork that captures the flow patterns of the full DFN, based primarily on node centrality features in the graph. Our supervised learning techniques train on particle-tracking backbone paths found by DFNWORKS, but run in negligible time compared to those simulations. We find that our predictions can reduce the network to approximately 20% of its original size, while still generating breakthrough curves consistent with those of the original network. [ABSTRACT FROM AUTHOR]

We investigate and characterize the influence of capillary end effects on steady-state relative permeabilities obtained in pore-scale numerical simulations of two-phase flows. Our study is motivated by the observation that capillary end effects documented in two-phase laboratory-scale experiments can significantly influence permeability estimates. While numerical simulations of two-phase flows in reconstructed pore-spaces are increasingly employed to characterize relative permeabilities, a phenomenon which is akin to capillary end effects can also arise in such analyses due to the constraints applied at the boundaries of the computational domain. We profile the relative strength of these capillary end effects on the calculation of steady-state relative permeabilities obtained within randomly generated porous micro-structures using a finite volume-based two-phase flow solver. We suggest a procedure to estimate the extent of the regions influenced by these capillary end effects, which in turn allows for the alleviation of bias in the estimation of relative permeabilities. [ABSTRACT FROM AUTHOR]

We present an analysis and visualization prototype using the concept of a flow topology graph (FTG) for characterization of flow in constrained networks, with a focus on discrete fracture networks (DFN), developed collaboratively by geoscientists and visualization scientists. Our method allows users to understand and evaluate flow and transport in DFN simulations by computing statistical distributions, segment paths of interest, and cluster particles based on their paths. The new approach enables domain scientists to evaluate the accuracy of the simulations, visualize features of interest, and compare multiple realizations over a specific domain of interest. Geoscientists can simulate complex transport phenomena modeling large sites for networks consisting of several thousand fractures without compromising the geometry of the network. However, few tools exist for performing higher-level analysis and visualization of simulated DFN data. The prototype system we present addresses this need. We demonstrate its effectiveness for increasingly complex examples of DFNs, covering two distinct use cases – hydrocarbon extraction from unconventional resources and transport of dissolved contaminant from a spent nuclear fuel repository. [ABSTRACT FROM AUTHOR]

ABSTRACT Restaurant foods are typically higher in calories than meals consumed at home. A goal of the 2010 Patient Protection and Affordable Care Act is to encourage healthier food choices at restaurants by providing consumers with information about the calorie content of menu items. However, doesn't a well-informed consumer already know that a 12-piece bucket of chicken has more calories than a salad? Following research in marketing science and behavioral economics, we evaluate a representative consumer's ability to discriminate between low-calorie and high-calorie menu items using only some basic 'rules of thumb' nutrition knowledge. Results reveal the extent to which menu labeling may increase consumers' knowledge about the calories in restaurant foods. [ABSTRACT FROM AUTHOR]

Although hydraulic fracturing has been used for natural gas production for the past couple of decades, there are significant uncertainties about the underlying mechanisms behind the production curves that are seen in the field. A discrete fracture network-based reservoir-scale work flow is used to identify the relative effect of flow of gas in fractures and matrix diffusion on the production curve. With realistic three-dimensional representations of fracture network geometry and aperture variability, simulated production decline curves qualitatively resemble observed production decline curves. The high initial peak of the production curve is controlled by advective fracture flow of free gas within the network and is sensitive to the fracture aperture variability. Matrix diffusion does not significantly affect the production decline curve in the first few years, but contributes to production after approximately 10 years. These results suggest that the initial flushing of gas-filled background fractures combined with highly heterogeneous flow paths to the production well are sufficient to explain observed initial production decline. These results also suggest that matrix diffusion may support reduced production over longer time frames. [ABSTRACT FROM AUTHOR]

The publication of this article unfortunately contained a mistake. The assignment to the affiliations of author Satish Karra was not correct [ABSTRACT FROM AUTHOR]

We analyze the statistical scaling of structural attributes of virtual porous microstructures that are stochastically generated by thresholding Gaussian random fields. Characterization of the extent at which randomly generated pore spaces can be considered as representative of a particular rock sample depends on the metrics employed to compare the virtual sample against its physical counterpart. Typically, comparisons against features and/patterns of geometric observables, e.g., porosity and specific surface area, flow-related macroscopic parameters, e.g., permeability, or autocorrelation functions are used to assess the representativeness of a virtual sample, and thereby the quality of the generation method. Here, we rely on manifestations of statistical scaling of geometric observables which were recently observed in real millimeter scale rock samples [] as additional relevant metrics by which to characterize a virtual sample. We explore the statistical scaling of two geometric observables, namely porosity ( ϕ) and specific surface area ( SSA), of porous microstructures generated using the method of Smolarkiewicz and Winter [] and Hyman and Winter []. Our results suggest that the method can produce virtual pore space samples displaying the symptoms of statistical scaling observed in real rock samples. Order q sample structure functions (statistical moments of absolute increments) of ϕ and SSA scale as a power of the separation distance (lag) over a range of lags, and extended self-similarity (linear relationship between log structure functions of successive orders) appears to be an intrinsic property of the generated media. The width of the range of lags where power-law scaling is observed and the Hurst coefficient associated with the variables we consider can be controlled by the generation parameters of the method. [ABSTRACT FROM AUTHOR]

The aim of this study was to apply and evaluate a recently developed prioritization model which uses the synoptic approach to geographically prioritize watersheds in which Best Management Practices (BMPs) can be implemented to reduce water quality problems resulting from erosion and sedimentation. The model uses a benefit-cost framework to rank candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most water quality improvement per conservation dollar invested. The model was developed to prioritize BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS Conservation Effects Assessment Project (CEAP). We applied the model to HUC-8 watersheds within the southeastern Coastal Plain ecoregion (USA) because not only is it an important agricultural area but also because it contains a well-studied medium-sized CEAP watershed which is thought to be representative of the ecoregion. The results showed that the three HUC-8 watersheds with the highest rankings (most water quality improvement expected per conservation dollar invested) were located in the southern Alabama, northern Florida, and eastern Virginia. Within these watersheds, measures of community attitudes toward conservation practices were highly ranked, and these indicators seemed to push the watersheds to the top of the rankings above other similar watersheds. The results, visualized as maps, can be used to screen and reduce the number of watersheds that need further assessment by managers and decision-makers within the study area. We anticipate that this model will allow agencies like USDA-NRCS to geographically prioritize BMP implementation efforts. [ABSTRACT FROM AUTHOR]

We introduce the feature rejection algorithm for meshing (FRAM) to generate a high quality conforming Delaunay triangulation of a three-dimensional discrete fracture network (DFN). The geometric features (fractures, fracture intersections, spaces between fracture intersections, etc.) that must be resolved in a stochastically generated DFN typically span a wide range of spatial scales and make the efficient automated generation of high-quality meshes a challenge. To deal with these challenges, many previous approaches often deformed the DFN to align its features with a mesh through various techniques including redefining lines of intersection as stair step functions and distorting the fracture edges. In contrast, FRAM generates networks on which high-quality meshes occur automatically by constraining the generation of the network. The cornerstone of FRAM is prescribing a minimum length scale and then restricting the generation of the network to only create features of that size and larger. The process is fully automated, meaning no adjustments of the mesh are performed, and the meshing of the individual fractures has been parallelized. Reported mesh statistics show that the computational meshes generated using FRAM are of high quality. Furthermore, the method does not require solving additional systems of linear equations which is needed if a nonconforming mesh is used. Details of the FRAM approach are provided, including its mathematical underpinnings and an algorithm for its implementation. We demonstrate the method's applicability by generating a DFN with similar statistics as the fractured granite at the Forsmark site in Sweden containing 6 700 fractures, and also solve the fully saturated Darcy flow equations to show the quality of the flow solutions which result from computation on the mesh. [ABSTRACT FROM AUTHOR]

Three phenomenological power law models for the permeability of porous media are derived from computational experiments with flow through explicit pore spaces. The pore spaces are represented by three-dimensional pore networks in 63 virtual porous media along with 15 physical pore networks. The power laws relate permeability to (i) porosity, (ii) squared mean hydraulic radius of pores, and (iii) their product. Their performance is compared to estimates derived via the Kozeny equation, which also uses the product of porosity with squared mean hydraulic pore radius to estimate permeability. The power laws provide tighter estimates than the Kozeny equation even after adjusting for the extra parameter they each require. The best fit is with the power law based on the Kozeny predictor, that is, the product of porosity with the square of mean hydraulic pore radius. [ABSTRACT FROM AUTHOR]

Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area's biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested. [ABSTRACT FROM AUTHOR]

Background. Bisphenol A (BPA) and other related chemical compounds may be components used in the manufacturing process of resin-based composite dental restorative material. The purpose of the authors' study was to assess salivary and urinary concentrations of BPA and other compounds before and after placement of resin-based composite dental restorations. Methods. The authors collected saliva and urine from 172 participants receiving composite restorations before and as long as 30 hours after placement of composite restorations. The authors analyzed saliva specimens from 151 participants and urine specimens from 171 participants for concentrations of BPA and five related compounds by using liquid chromatography/mass spectrometry (LC/MS). Results. Salivary concentrations of BPA and some related compounds increased immediately (within one hour) after composite placement. Salivary concentrations of BPA and most study compounds returned to prerestoration levels within eight hours after composite placement. With the exception of a 43 percent increase in BPA, concentrations of the study compounds in urine returned to prerestoration levels nine to 30 hours after restoration placement. Concentrations in saliva were lower when a rubber dam was used; however, rubber dam use appeared to have no effect on urinary concentrations of the measured compounds during the study period. The authors observed similar changes in study compound levels in both saliva and urine between participants who received anterior restorations and those who received posterior restorations. Conclusions. Placement of resin-based composite restorations was associated with detectable increases in saliva of BPA and other study compounds within one hour after restoration placement and an increased concentration of BPA in urine nine to 30 hours after restoration placement. Rubber dam use did not reduce the absorption of BPA (measured as BPA level in urine) during the study. Clinical Implications. Additional studies are needed to address how long BPA levels in urine associated with composite placement remain elevated to aid in better understanding of the clearance rates of BPA and other study compounds. [ABSTRACT FROM AUTHOR]

Objectives. The goal of this study was to investigate and identify risk factors for suicide among all active duty members of the US military during 2005 or 2007. Methods. The study used a cross-sectional design and included the entire active duty military population. Study sample sizes were 2 064 183 for 2005 and 1 981 810 for 2007. Logistic regression models were used. Results. Suicide rates for all services increased during this period. Mental health diagnoses, mental health visits, selective serotonin reuptake inhibitors (SSRIs), sleep prescriptions, reduction in rank, enlisted rank, and separation or divorce were associated with suicides. Deployments to Operation Enduring Freedom or Operation Iraqi Freedom were also associated with elevated odds ratios for all services in the 2007 population and for the Army in 2005. Conclusions. Additional research needs to address the increasing rates of suicide in active duty personnel. This should include careful evaluation of suicide prevention programs and the possible increase in risk associated with SSRIs and other mental health drugs, as well as the possible impact of shorter deployments, age, mental health diagnoses, and relationship problems. [ABSTRACT FROM AUTHOR]

Food loss at the retail and consumer levels in the United States includes 14.8 billion pounds of fruit and 23.4 billion pounds of vegetables, valued at $15.1 billion and $27.7 billion, respectively, in 2008 retail market prices. The total value of these losses is $42.8 billion per year, or roughly $141 per capita. To most efficiently reduce the annual food loss, it may be beneficial to focus efforts on the four fruits (fresh apples, grapes, peaches and strawberries) and four vegetables (fresh and canned tomatoes and fresh and frozen potatoes) that have the greatest amount of loss. [ABSTRACT FROM AUTHOR]

Animal migrations frequently cross international boundaries, and conservation of migratory species requires the collaborative efforts of multiple nations. The Convention on Migratory Species (CMS) has overseen such conservation efforts for over twenty-five years by encouraging member Parties to conclude daughter agreements focused on protecting individual migratory species or groups of migratory species. In the past twenty-five years, CMS members have concluded twenty-six agreements that protect a wide range of migratory species. Many of these agreements provide targeted actions to offer immediate protection for critically endangered or threatened migratory species, but a handful are much broader in scope, providing protection for large classes of migratory species, regardless of endangerment status. This Article examines the structure of the CMS and its daughter agreements to identify key challenges for international migratory species protection, and draws on international environmental policy literature to identify potential strategies for overcoming these problems in future agreements. [ABSTRACT FROM AUTHOR]

Animal migrations capture the human mind and heart like few other natural phenomena. Migrations provide ecological, psychological (e.g., aesthetic), cultural, and economic benefits. Increasingly, though, migrations are being recognized as threatened phenomena-that is, spectacular aspects of the life history of animal species often involving large numbers of individuals, but which are threatened with impoverishment or demise, even though the species per se may not be in peril Migration phenomena are themselves worthy of protection, as a category of biodiversity. Yet, conserving mignitory populations and there migrations is particularly problematic. Migratory animals are especially vulnerable to a variety of threats because they come into contact with multiple ecosystems and jurisdictions, tend to congregate in large numbers in discrete and often vulnerable areas, and require considerable fuel for their long-distance journeys. In addition, immigration is essentially a phenomenon of abundance-the benefits and values of migrations depend on an abundance of animals taking part-and conserving species' populations before they become rare has always been an uphill battle. This Article presents an idea for a new federal law that reflects the perspective that conserving migratory behaviors and processes as phenomena of value in and of themselves, and not only of value for species persistence, can provide unique and important benefits. Current conservation laws generally serve the species-based conservation perspective and, with a few exceptions, are not designed or implemented to protect benefits of abundant animal migrations. The existing fragmented framework of laws and authorities also is insufficient to protect most migratory populations against a diversity of threats across multiple jurisdictions and broad geographic scales. Our proposed federal law would offer a unified framework, require abundance targets, and authorize a comprehensive set of legal tools, including both carrots and sticks, for conserving a limited set of nationally or regionally "significant" migrations. Such a law would likely improve the current situation for the nation's most notable migratory populations and generally promote the conservation of all migrations as phenomena of abundance. [ABSTRACT FROM AUTHOR]