Advances in Water Resources最新文献

Increasing flood risk due to urbanization and climate change poses a significant challenge to societies at global scale. Hydrologic information that is required for understanding flood processes and for developing effective warning procedures is currently lacking in most parts of the world. Procedures that can combine global climate dataset from satellite and reanalysis with fast and low computational cost prediction systems, are attractive solutions for addressing flood predictions in ungauged areas. This work develops and tests a prediction framework that relies on two fundamental components. First, meteorological data from global datasets (IMERG and ERA5-Land) provide key input variables and second, ML models trained in the data-rich contiguous US, are applied in climatically similar regions in other parts of the world. Catchments in Australia, Brazil, Chile, Switzerland, and Great Britain were used as pseudo-ungauged regions for testing. Results indicate acceptable performance for both IMERG and ERA5-Land forced models with relative difference in flood peak prediction within 30 % and similar overall performance to locally trained ML models. Specific climate regions for which ML models have revealed good performance include Mediterranean climates like the US West Coast, subtropical areas like the Southern Atlantic Gulf, and mild temperate regions like the Mid-Atlantic Basin. This work highlights the potential of combining global precipitation dataset with pre-trained ML models in data-rich areas, for flood prediction in ungauged areas with similar climate.

Pore networks play a key role in understanding and quantifying flow and transport processes in complex porous media. Realistic pore-spaces may be characterized by singular regions, that is, isolated subnetworks that do not connect inlet and outlet, resulting from unconnected porosity or multiphase configurations. The robust identification of these features is critical for the characterization of network topology and for the solution of the set of linear equations of flow and transport. We propose a robust method based on singular value decomposition to solve for network flow and locate isolated subnetworks simultaneously. The performance of the method is demonstrated for pore networks of different complexity.

The overall benefits of geological carbon storage (GCS) depend primarily on CO₂ storability and injectability, expressed as saturation and relative permeability, respectively. The effects of GCS schemes on these two properties, the macroscopic response indicators of a two-phase seepage system, are closely related to pore-scale two-phase behaviors. However, the comprehensive effects of capillary number (Ca) and wettability (θ) on saturation and relative permeability are poorly understood. Here we proposed a digital rock physics (DRP) technique workflow for the phase field method and systematically investigated how these effects control two-phase seepage at pore scale through the high-resolution visualization results obtained. We created a Ca-θ phase diagram identified by four pore-scale displacement mechanisms, including finger-like invasion, burst, cooperative filling and coexistence of concave and convex interfaces, to illustrate the comprehensive effects of Ca and θ. We found that the relative permeability of the defending phase (water in this work) is determined by the net effect of the direct driving and viscous coupling effects. We organized comprehensive Ca-θ diagrams and revealed the favorable conditions for CO₂ injectability and storability. Our results demonstrate that GCS schemes, mainly about capillary number and wettability, can significantly influence CO₂ storage performance via the two-phase flow at pore scale, which should be considered carefully. This work provides valuable insights into the selection of an optimal GCS scheme and contributes to an in-depth understanding of multiphase seepage at pore scale.

Hydraulic tomography (HT) has been proven effective for characterizing aquifer hydraulic heterogeneity for decades. Many have also proposed using electrical resistivity tomography (ERT) to gain prior information about large-scale layer structures to improve the HT estimates, when the number of pumping tests and drawdown measurements is limited. This study investigates the merits of such proposals via numerical and physical experiments using an actual sandbox.

The numerical experiments show that ERT can detect the sandbox's layer structure under fully saturated conditions. Surprisingly, the physical sandbox experiments yielded different results: ERT could not detect the layer structures under saturated conditions. Nevertheless, the surveys in the physical experiments under drained conditions facilitated mapping structures, revealing that electrical conductivity show a stronger correlation with moisture content than porosity. We then used the detected layer structure under unsaturated conditions from the ERT without a definitive resistivity/hydraulic conductivity relationship as the HT prior information to improve HT's saturated hydraulic conductivity (K) estimates. The results of independent pumping and tracer tests in the numerical sandbox experiments demonstrated and validated improvements in K estimation. These findings are significant, questioning the need for accurate local-scale resistivity/hydraulic conductivity relationships. It is a new insight into hydrogeophysics.

Numerical simulations explore for the first time the role of mega columns and aperture variability on particle transport through mature volumetric contraction networks as informed by a unique synthesis of network propagation and maturity. Columnar fracture patterns are generated by updating a series of Voronoi centers to the midpoint of a generated polygon over many iterations, creating 250 network realizations. A DFN simulator solves for fluid flow and tracks conservative particle trajectories within each network. Dominant fracture attributes impacting fluid flow and solute transport in volumetric contraction networks are fracture orientation, density, and aperture/transmissivity. Ensemble plume snapshots generated by networks with equal fracture transmissivity define a baseline-level of dispersion that is solely attributed to network structure and connectivity. Longitudinal and transverse dispersion increase and the center of plume mass becomes delayed relative to the baseline case when fracture transmissivity is varied according to a lognormal distribution. The incorporation of highly-transmissive, large-aperture mega column fractures leads to plume snapshots with a more pronounced leading edge and an order of magnitude faster average breakthrough times. The breakthrough curves contain three peaks reflecting contrasting transport pathways in which particles are: (i) initially placed in mega column fractures and remain in these features until exiting the model domain, (ii) initially placed into small column fractures, incur additional time to migrate and enter a mega column fracture, and remain within those mega columns, and (iii) initially placed in small column fractures and remain in these fractures. Incorporating variability in fracture transmissivity for both small column and mega column fractures disrupts the binary distinction between small column and mega column fracture velocities and leads to dispersed breakthroughs over long time scales with a single peak. These results demonstrate that preferential flow paths emerge in volumetric contraction networks due to contracts in fracture transmissivity, not fracture connectivity as observed in tectonic networks.

Multispecies transport analytical models that solve advection-dispersion equations (ADEs) are efficient tools for evaluating the transport of decaying contaminants and their sequential products. This study develops a novel semi-analytical model to simulate the multispecies transport of decaying contaminants, considering nonequilibrium sorption and decay in both dissolved and sorbed phases. First-order reversible kinetic sorption equations with decay processes are coupled to ADEs. Recursive analytical solutions, using the Laplace transform and generalized integral transform, are developed to address the mathematical complexity of the governing equations. The model's simulation results show excellent agreement with both numerical models and existing analytical solutions. Applied to a four-member radionuclide decay chain, the model reveals that including decay in the sorbed phase results in a lower concentration of the first member and avoids underestimating the radioactivity concentrations of daughter elements. These differences in dissolved radioactivity concentrations between models with and without sorbed phase decay may impact health risk assessments for radioactive waste disposal. Finally, this study provides a more sophisticated mathematical tool for analyzing multispecies transport in real field conditions where nonequilibrium sorption processes predominantly occur.

Modelling non-Darcy flow behaviour in shale rocks, composed of nanometer-sized pores and multi-scale fracture networks, is crucial for various subsurface energy applications. However, incorporating multiple physical mechanisms across numerous scales is not trivial. This work proposes an improved and practical upscaling workflow for coupling an Upscaled Discrete Fracture Matrix (UDFM) model and a pressure-dependent apparent permeability (K_app) model to capture the effects of non-Darcy flow in multi-scale fractured shale reservoirs.

First, a 3D DFN is upscaled into octree-refined continuum meshes, where equivalent rock parameters and rock-fluid functions are defined using the UDFM approach. Then, the flow simulation is coupled with a pressure-dependent K_app updating scheme using an existing K_app model and a multiple-restart technique. The effects of non-Darcy flow mechanisms (e.g., slip flow, transitional flow, Knudsen diffusion) are captured. The constructed models are then used to study the impacts of fracture network connectivity and pressure interference on production. The results of this new approach are compared against those obtained from another commercial package while preserving the advantages of DFNWORKS. Neglecting non-Darcy flow behaviours could significantly underestimate gas production and water recovery. It is illustrated that the nanoscale flow mechanisms help to enhance matrix-matrix and matrix-fracture flow. The constructed models are also utilized to study the effects of disconnected or isolated fractures, pressure interference, water retention, and shut-in durations on well performance. The proposed flexible strategies can be adopted in other commercial/open-source fractured-porous-media subsurface-flow simulation frameworks.

Transport phenomena in unsaturated porous media still present an important research topic. In particular, in the context of recent environmental concerns, further understanding of contaminant transport in the partially saturated vadose zone is necessary. However, there is currently a lack of understanding of the relationship between water saturation, in particular the two-phase distribution, and dispersion. This is due to the intricate interactions between the two-phase flow and the porous structure, as well as the complexity of the experimental techniques, which prevents a significant number of configurations from being analysed.

We explore passive tracer transport in two-dimensional unsaturated porous media via experimental and numerical methods. To this goal, we conduct co-injection experiments to produce two-phase distributions (air/water) at different saturations in a transparent micromodel that mimics the topology of the Bentheimer sandstone. From these experiments, we generate images using multi-scale multiple-point statistics modelling (MPS). Employing the Lattice Boltzmann method, we calculate velocity and concentration fields for both experimental and generated images under saturated and unsaturated conditions. Our results show strong similarities in velocity distributions, good agreement in concentration profiles, and a matching of dispersion characteristics between experimental and MPS-generated images. MPS enables us to create a variety of unsaturated porous media structures with different topologies but similar transport properties. From these images, we analyse transport over a large range of saturations and Peclet numbers. We observe pre-asymptotic non-Fickian transport regimes characterized by a variance increasing with time according to a power law with exponent $\alpha >1$. We find that $\alpha$ increases as saturation decreases, due to enhanced flow heterogeneity, and with higher Peclet numbers. This behaviour is confirmed through large-scale simulations.

This paper focuses on the modeling of hydrogen (H2) storage in subsurface formations, particularly focusing on the equilibrium between H2 and brine and its implications for hydrogen transport properties in black-oil reservoir simulations. Initially, we evaluate and calibrate various equations of state (EoS) for H2-water and H2-brine mixtures. Our analysis ranges from the molecular-level Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation to a more explicit version of the Redlich–Kwong cubic EoS, and concludes with an empirical Henry–Setschenow (HS) model. These models are compared in terms of their ability to predict mutual solubilities with validation against experimental data. This study compares the strengths and limitations of each thermodynamic model, highlighting their overall good predictability across various temperatures, pressures, and salinity levels with a relatively moderate number of adjustable parameters. Subsequently, we apply these thermodynamic models to generate Pressure–Volume–Temperature (PVT) phase equilibrium data for use in black-oil simulations, focusing on the behavior of H2 in saline aquifers. Our investigation examines the effects of salt concentration, H2 solubility, molecular diffusion, and the impact of cycling frequency, injection and withdrawal rates on the storage and recoverability process. We present three numerical examples to illustrate these concepts: a 2D aquifer model, a modified benchmark originally designed for simulating the conversion of natural gas to hydrogen storage, and a 3D anticlinal dome-shaped aquifer model. These examples cover a range of complexities, such as heterogeneous permeability, porosity variations, and diverse rock types with specific entry pressures, providing a comprehensive overview of the factors influencing H2 storage in subsurface formations.

Solute transport in unsaturated porous media is of interest in many engineering and environmental applications. The interplay between small-scale, local forces and the porous microstructure exerts a strong control on the transport of fluids and solutes at the larger, macroscopic scales. Heterogeneity in pore geometry is intrinsic to natural materials across a large range of scales. This multiscale nature, and the intricate links between two-phase flow and solute transport, remain far from well understood, by and large. Here, we use high-resolution direct simulation to quantify solute mixing and dispersion behavior within correlated porous media during drainage under an unfavorable viscosity ratio. Through analysis of flow and transport at multiple realizations, we find that increasing spatial correlations in pore sizes increase the size of the required Representative Elementary Volume (REV). We show that increasing the correlation length enhances solute dispersivity through its impact on the spatial distribution of low-velocity (diffusion-dominated) and high-velocity (advection-dominated) regions. Fluid saturation is shown to directly affect diffusive mass flux among high- and low-velocity zones. Another indirect effect of correlated heterogeneity on solute transport is through its control of the drainage patterns via repeated alteration in the connectivity of flowing pathways. Our findings improve quantitative understanding of solute mixing and dispersion under two-phase conditions, highly relevant to some of our most urgent environmental problems.