•The scale dependency of fluid flow and reactive solute transport parameters was discussed across scales.•A clear guideline was provided for selecting appropriate upscaling methods for practical ...applications.•Functions, assumptions, and limitations of deterministic and stochastic upscaling methods were addressed comparatively.•Critical insights into the scaling issues were prospected for modeling fluid flow in multi-scale subsurface systems.
Physical and biogeochemical heterogeneity dramatically impacts fluid flow and reactive solute transport behaviors in geological formations across scales. From micro pores to regional reservoirs, upscaling has been proven to be a valid approach to estimate large-scale parameters by using data measured at small scales. Upscaling has considerable practical importance in oil and gas production, energy storage, carbon geologic sequestration, contamination remediation, and nuclear waste disposal. This review covers, in a comprehensive manner, the upscaling approaches available in the literature and their applications on various processes, such as advection, dispersion, matrix diffusion, sorption, and chemical reactions. We enclose newly developed approaches and distinguish two main categories of upscaling methodologies, deterministic and stochastic. Volume averaging, one of the deterministic methods, has the advantage of upscaling different kinds of parameters and wide applications by requiring only a few assumptions with improved formulations. Stochastic analytical methods have been extensively developed but have limited impacts in practice due to their requirement for global statistical assumptions. With rapid improvements in computing power, numerical solutions have become more popular for upscaling. In order to tackle complex fluid flow and transport problems, the working principles and limitations of these methods are emphasized. Still, a large gap exists between the approach algorithms and real-world applications. To bridge the gap, an integrated upscaling framework is needed to incorporate in the current upscaling algorithms, uncertainty quantification techniques, data sciences, and artificial intelligence to acquire laboratory and field-scale measurements and validate the upscaled models and parameters with multi-scale observations in future geo-energy research.
•A Lagrangian-based transport model is derived for simulating contaminant transport in natural fracture.•The upscaled dispersivity is higher than the measured dispersivity.•LBTM can directly estimate ...field-scale dispersivity based on geostatistical data.
Physical heterogeneities are prevalent features of fracture systems and significantly impact transport processes in aquifers across different spatiotemporal scales. Upscaling solute transport parameter is an effective way of quantifying parameter variability in heterogeneous aquifers including fractured media. This paper develops conceptual models for upscaling conservative transport parameters in fracture media. The focus is on upscaling dispersivity. Lagrangian-based transport model (LBTM) for dispersivity upscaling are derived for the solute transport in two-dimensional fractures surrounded by an impermeable matrix. The LBTM is validated against the random walk particle tracking (RWPT) model, which enables highly efficient and accurate predictions of conservative solute transport. The results show that the derived scale-dependent analytical expressions are in excellent agreement with RWPT model results. In addition, LBTM results are also compared to experimental results from the observed breakthrough curve of a conservative solute transport through a single natural fracture within a granite core. Comparing results from the LBTM and transport experiment shows that LBTM based estimated dispersivity is 10.55% higher than the measured value. Errors introduced by the experiments, the conceptual assumptions in deriving models, and the heterogeneities of fracture apertures not fully sampled by measuring instruments are main factor for such discrepancy. The sensitivity analysis indicates that the longitudinal and transverse dispersivities are positively related to the integral scale and the variance of the log-fracture aperture. The longitudinal dispersivity is strongly contolled by the variance of the log-fracture aperture. The LBTM may be useful for directly predicting solute transports, requiring only the acquisition of fractured geostatistical data. This work provides a better understanding of transport processes in fractured media which ultimately control water quality across scales.
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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 spreading
Neglecting capillary heterogeneity leads to overestimation of atmospheric CH4 and underestimation of mobile gaseous CH4 in aquifer
Meter‐scale sedimentary stratification contributes more to CH4 plume migration than millimeter‐ and centimeter‐scale features
We study the effect of brine composition on CO2 dissolution in brine. In particular, we address the effect of brine composition on the onset of convection through experiments, numerical simulations, ...and theoretical analyses. We use two brine solutions—one containing sodium chloride (NaCl) and one containing mixtures of NaCl and calcium chloride (CaCl2)—to study their differences in terms of the onset of convection. We perform experiments in isothermal conditions (∼50 C) with pressure range of 500–535 psi for different salinities and permeabilities (Rayleigh number of 2900 to 4900). Our experimental conditions and set-up design allow us to avoid problems associated with analogue fluids as well as with blind cells. We also conduct linear stability analysis (LSA) and high-resolution direct numerical simulations (DNS). We analyze pressure data and calculate other parameters such as diffusion coefficient, viscosity, and solubility. Specifically, we obtain the onset of convection from pressure decay curves and critical wave number from image analyses. We show that the onset of convection occurs earlier with higher wave number in brine solutions containing NaCl. Pressure results show that using mixture of NaCl and CaCl2 results in a higher CO2 diffusion coefficient, which in turn damps convective instabilities. Thus, the onset time of instabilities is later and finger growth rate is smaller for brines with NaCl and CaCl2. Our DNS results show that deviation between the cumulative dissolved CO2 as well as the dissolved CO2 due to only diffusion process occurs earlier for NaCl solution. We found a dimensionless Rayleigh-dependent onset of instability with parameters that are close for two mixtures. However, differences in the CO2 diffusivity result in smaller Rayleigh numbers for NaCl and CaCl2 containing mixtures. Our results have practical implications for CO2 geological sequestration in saline aquifers.
•High-pressure visual set up is implemented to examine the effect of brine composition on the onset of convection.•Scaling relationships for onset of instability and onset of convection with corresponding wave numbers are introduced.•Diffusion coefficient of CO2 is introduced as an important factor which controls the onset of convection.
•Previous groundwater intelligent models often overlook the inherent limitations.•Integrating data, humans and machines can enhance groundwater modeling.•Multisource data serves as the training basis ...and insight generator.•Human expertise acts as the source of intelligence and collaborative catalyst.•Machine intelligence is the cornerstone of high accuracy and efficiency.
Groundwater models are essential for understanding aquifer systems behavior and effective water resources spatio-temporal distributions, yet they are often hindered by challenges related to model assumptions, parametrization, uncertainty, and computational efficiency. Machine intelligence, especially deep learning, promises a paradigm shift in overcoming these challenges. A critical examination of existing machine-driven methods reveals the inherent limitations, particularly in terms of the interpretability and the ability to generalize findings. To overcome these challenges, we develop a ternary framework that synergizes the valuable insights from multisource data, human expertise, and machine intelligence. This framework capitalizes on the distinct strengths of each element: the value and relevance of multisource data, the innovative capacity of human expertise, and the analytical efficiency of machine intelligence. Our goal is to conceptualize sustainable water management practices and enhance our understanding and predictive capabilities of groundwater systems. Unlike approaches that rely solely on abundant data, our framework emphasizes the quality and strategic use of available data, combined with human intellect and advanced computing, to overcome current limitations and pave the way for more realistic groundwater simulations.
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Permeability is an important hydrogeological parameter for the quantitative evaluation of water resources and prediction of water inflow. In this study, we examine a typical water-bearing sandstone ...obtained in North China to explore the correlation between the microscopic pore characteristics and macroscopic permeability of the sandstone. In addition, pixel-level annotated data are generated from the images obtained in electron microscopy experiments for deep learning training. Using the deep learning framework, we analyse the pore characteristics through semantic image segmentation based on artificial intelligence and explore the relationship between the microscopic pore characteristics and the macroscopic permeability parameters of the sandstone. This method addresses the limitations of traditional image recognition methods, such as the inability to obtain the complete pore space characteristics in scanning electron microscopy (SEM) images as well as poor segmentation and low accuracy. Moreover, this method can be used to realise the full benefits of accurate image recognition, and it enables the automatic processing of microscopic images to significantly improve the accuracy of pore identification in rock samples.
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•Pore structure of transitional shale and organic matter is measured by N2, CO2 isotherms.•Both organic and inorganic grains develop micro-, meso- and macropores in gas window.•Clay ...minerals control specific surface area and absorbed gas at over-mature stage.•High TOC increases micropore volume at high depth, while total pore volume decreases.
The recoverable resource of shale gas is 25 trillion cubic meter, 33% of which is stored in transitional shales in China. This work investigates the effects of organic and inorganic compositions on the development of Upper Paleozoic transitional shale pore structures through a combination of petrophysical and geochemical measurements. 42 shale samples were collected from marsh-lagoon and coastal delta settings in the Ordos Basin, NW China. The samples include the Upper Permian Shanxi shale (average total organic carbon (TOC) of 1.58wt%, Type III kerogen, average vitrinite reflectance (Ro) 2.6%), and the Upper Carboniferous Benxi shale (average TOC of 1.91wt%, Type III kerogen, average Ro 2.74%) at the over-mature stage or dry gas window. An important characteristic of these shales is the large proportion of clay minerals (∼69% in Benxi shale and 54% in Shanxi shale). The quartz content is ∼17% and 40% for Benxi and Shanxi shales, respectively. The pore structure of three samples and one isolated kerogen sample is analyzed via both low-pressure nitrogen and carbon dioxide adsorption methods. Low pressure nitrogen adsorption experiments show that Benxi and Shanxi shales characterized by ultra-low porosity and permeability develop mainly silt-shaped pores and potentially ink-bottle-shaped pores. We find that increasing fractions of organic matter (OM) result in a decrease in both total pore volume and specific surface area (SSA). Low pressure carbon dioxide adsorption experiments show that micropore volumes nonlinearly increase with increasing OM, although the contribution of organic micropore volume is limited. The mesopore and macropore volumes of inorganic compositions contribute mostly to the total pore volume. The OM in transitional shales in Yanchang mainly develop mesopores (with <5nm diameters), which significantly contribute to the SSA, while micropores are the main contributor to SSA in the inorganic matter. For thermally over-mature transitional shales, clay minerals contribute the most to SSA and pore volume as well as the storage capacity of absorbed and free gas.
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 investigated
Silt sediments promote denitrification since they experience longer residence times and are source of dissolved organic carbon
Streambeds with mixture of sand and silt have high capacity to remove nitrate despite moderate permeabilities
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•We present the most comprehensive study on modeling the IFT between CO2 and formation brine.•Seven predictive models based on 2517 experimental data collected here are ...developed.•CMIS and GMDH models present, respectively, the highest and lowest accuracy.•The developed models are able to reveal and capture the non-trivial behavior of IFT
Global climate change is a pressing problem, and given the current state of development of renewable energies, Carbon Capture and Storage (CCS) appears a promising solution. Deep saline aquifers are the most accessible geological locations for safe CO2 sequestration, where CO2 can be securely trapped mostly by structural and capillary trapping mechanisms. The efficiency of both mechanisms is governed by multiphase flow characteristics that are heavily dependent on interfacial tension (IFT) between the gaseous and aqueous phases. In this study, we conduct a comprehensive data-driven study on the interfacial tension of pure and impure gas-brine mixtures within saline aquifers. We carefully collect 2517 experimental data, upon which we develop seven machine-learning based models for predicting the IFT. These include, namely, four differently optimized multilayer perceptron models, a radial basis function neural network, a least squares support vector machine, and a group method of data handling-type neural network. The three most accurate intelligent models are subsequently incorporated into a state-of-the-art committee machine intelligent system. We use a pseudo (average) critical temperature variable to capture the gas phase impurities while developing a general-in-purpose predictive tool for any gas mixture. We comprehensively evaluate the accuracy and statistical validity of our models, and demonstrate their robustness relative to major empirical correlations currently in use. The analyses reveal the non-trivial behavior of IFT against various parameters. Finally, we elucidate the impact of gas and brine impurities on the storage capacity of saline aquifers at various in-situ conditions.
Groundwater serves as a primary source of public‐water and agricultural supply in many areas of Alabama, in particular during drought periods. Long‐term climatic models for the southeastern United ...States indicate that the region will be subjected to more intense and more frequent precipitation events, with no overall change in the amount of precipitation, resulting in increased runoff and reduced aquifer recharge. Reliable prediction of groundwater levels would be beneficial to water resources decision makers and stakeholders especially for time‐sensitive decisions. This paper uses a compound application of continuous wavelet transform (CWT) analysis and long short‐term memory (LSTM) framework to address the major question with regards to groundwater level: “how long does it take for groundwater to respond to major precipitation events and what is the magnitude of the response?” CWT analysis is used to answer the “how long” part in this question, while the LSTM is used to answer the “what is the magnitude” part of the question. The insights from CWT analysis related to the short‐term and long‐term response in groundwater level were used to set the parameters of the LSTM model. The LSTM model uses daily groundwater levels, precipitation, and maximum/minimum temperatures as input data. The model was able to provide predictions within a 95% confidence interval of actual groundwater levels. The findings of this study suggest a workflow for groundwater level forecasting in the wells of Alabama given a minimum amount of easy‐to‐measure and widely available data.
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