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"An Introduction to Solute Transport in Heterogeneous Geologic Media" is a comprehensive book that explores the topic of solute transport in geologic media. The book covers foundational concepts, methodologies, and real-world applications, providing a well-structured progression from basic to advanced topics. It includes exercises to reinforce theoretical concepts and practical scenarios. While the book could benefit from incorporating more recent research and digital interactivity, it is a valuable resource for students and professionals interested in understanding solute transport in complex geologic settings. [Extracted from the article]

The stochastic models and deep‐learning models are the two most commonly used methods for subsurface sedimentary structures identification. The results from the stochastic models typically involve uncertainty due to their nature. For the deep‐learning models, sufficient structure samples are necessary for training, but they are practically difficult to obtain. This study develops an inversion framework to combine the strength of these two models to overcome the limitations. The stochastic model is first adopted to generate the structure samples required by the deep‐learning models by integrating available observations. Then the trained deep‐learning model is utilized to reduce the uncertainty of the structures generated by the stochastic models. This integrated framework can successfully estimate the structures using available observations. Importantly, no additional structure training samples are required in the identification process. To summarize, the combination of the stochastic and the deep‐learning models shows great advantages in identifying subsurface sedimentary structures. Plain Language Summary: This study develops an integrated inversion framework that uses sparse data obtained from different geophysical measurements to identify subsurface sedimentary structures. By combining the advantages of physically‐based geological models and advanced deep‐learning techniques, the developed framework can identify complex subsurface sedimentary structures in a more practical way than is currently possible. The advantage of this method is that the identification process relies only on sparse measurement data and does not need additional structure samples for deep‐learning model training. It has significant practical value because most deep‐learning‐based methods require a large number of training samples, which are practically difficult to obtain. Therefore, the developed framework has great advantages in practical subsurface sedimentary structures identification applications. Key Points: A stage‐wise inversion framework is developed by combining the transition probability approach and the deep‐learning methodsThe developed framework can identify subsurface sedimentary structures using sparse geological data and flow and transport responsesThe estimated structures can honor the available flow and transport responses and capture the preferential flow pathways [ABSTRACT FROM AUTHOR]

The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model's superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity. [ABSTRACT FROM AUTHOR]

Rivers and their hyporheic zones play an important role in nutrient cycling. The fate of dissolved inorganic nitrogen is governed by reactions that occur in the water column and streambed sediments. Sediments are heterogeneous both in term of physical (e.g., hydraulic conductivity) and chemical (e.g., organic carbon content) properties, which influence water residence times and biogeochemical reactions. Yet few modeling studies have explored the effects of both physical and chemical heterogeneity on nutrient transport in the hyporheic zone. In this study, we simulated hyporheic exchange in physically and chemically heterogeneous sediments with binary distributions of sand and silt in a low‐gradient meandering river. We analyzed the impact of different silt/sand patterns on dissolved organic carbon, oxygen, nitrate, and ammonium. Our results show that streambeds with a higher volume proportion of silt exhibit lower hyporheic exchange rates but more efficient nitrate removal along flow paths compared to predominantly sandy streambeds. The implication is that hyporheic zones with a mixture of inorganic sands and organic silts have a high capacity to remove nitrate, despite their moderate permeabilities. Plain Language Summary: With the advent of intensive cultivation and livestock farms, nutrient contamination has increased in streams. Microbial communities in streambeds reduce nitrate, effectively removing it from streams. However, quantifying this removal is difficult because it is influenced by many spatially variable factors, among which sediment type plays an important role. Streambeds can contain both sandy and silty sediments distributed in complex patterns. The present study employs computer simulations to analyze how nitrate is removed in a mixture of different streambed sediments. Key Points: Role of physical and chemical heterogeneity of streambed sediments on denitrification is numerically investigatedSilt sediments promote denitrification since they experience longer residence times and are source of dissolved organic carbonStreambeds with mixture of sand and silt have high capacity to remove nitrate despite moderate permeabilities [ABSTRACT FROM AUTHOR]

Temperature is an important factor in designing and maintaining tunnels, especially in cold regions. We present three-dimensional numerical simulations of tunnel temperature fields at different temperature conditions. We study the tunnel temperature field in two different conditions with relatively low and high ambient temperatures representing winter and summer of northeast China. We specifically study how these temperature conditions affect tunnel temperature and its migration to surrounding rocks. We show how placing an insulation layer could affect the temperature distribution within and around tunnels. Our results show that the temperature field without using an insulation layer is closer to the air temperature in the tunnel, and that the insulation layer has shielding effects and could plays an important role in preventing temperature migration to surrounding rocks. We further analyzed how thermal conductivity and thickness of insulation layer control the temperature distribution. The thermal conductivity and thickness of insulation layer only affect the temperature of the surrounding rocks which are located at distances below ~20 m from the lining. [ABSTRACT FROM AUTHOR]

Stope backfill is important in avoiding mine collapse during and after extraction phases, ground subsidence in abandoned mines, and environmental damages. The stress distribution is one of the key factors in designing stope backfills. In this paper, we perform a numerical modeling study to investigate the stress distribution within and around the stope backfill. Importantly, our simulation results are in agreement with Marston's (1930) plain-strain arching theory. The results show that the stress arch is critical in stope backfills. The potential effects of internal friction angle, aspect ratio, and Poisson's ratio on stress distributions are also analyzed. The stress decreases when the aspect ratio, internal friction angle, and Poisson's ratio increase. Our results suggest that decreasing the aspect ratio and choosing materials with a high internal friction angle and Poisson's ratio are important for designing the stope backfill. The cohesive force index and elastic modulus also have significant effects on the stress distribution. Our findings have practical implications in designing stope backfills. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic model for multicomponent competitive monovalent cation exchange in hierarchical porous media. Reactive transport in porous media is highly sensitive to heterogeneities in physical and chemical properties, such as hydraulic conductivity ( K), and cation exchange capacity ( CEC). We use a conceptual model for multimodal reactive mineral facies and develop a Eulerian-based stochastic theory to analyze the transport of multiple cations in heterogeneous media with a hierarchical organization of reactive minerals. Numerical examples investigate the retardation factors and dispersivities in a chemical system made of three monovalent cations (Na, K, and Cs). The results demonstrate how heterogeneity influences the transport of competitive monovalent cations, and highlight the importance of correlations between K and CEC. Further sensitivity analyses are presented investigating how the dispersion and retardation of each cation are affected by the means, variances, and integral scales of K and CEC. The volume fraction of organic matter is shown to be another important parameter. The Eulerian stochastic framework presented in this work clarifies the importance of each system parameters on the migration of cation plumes in formations with hierarchical organization of facies types. Our stochastic approach could be used as an alternative to numerical simulations for 3D reactive transport in hierarchical porous media, which become prohibitively expensive for the multicomponent applications considered in this work. [ABSTRACT FROM AUTHOR]