Reactive Transport Simulations Research Articles

Artificial Neural Network Surrogate Model for Geochemical Calculations in Pore-Scale Reactive Transport Simulations

Artificial Neural Network Surrogate Model for Geochemical Calculations in Pore-Scale Reactive Transport Simulations

Artificial Neural Networks for Mineral Production Forecasting in the In Situ Leaching Process: Uranium Case Study

This study was conducted to assess the applicability of artificial neural networks (ANN) for forecasting the dynamics of uranium extraction over exploitation time during the process of In Situ Leaching (ISL). Currently, ISL process simulation involves multiple steps, starting with geostatistical interpolation, followed by computational fluid dynamics (CFD) and reactive transport simulation. While extensive research exists detailing each of these steps, machine learning techniques may offer the potential to directly obtain extraction curves (i.e., the concentration of the mineral produced over the exploitation time of the deposit), thereby bypassing these computationally expensive steps. As a basis, both an empirical experimental configuration and reactive transport simulations were used to generate training data for the neural network model. An ANN was constructed, trained, and tested on several test cases with different initial parameters, then the expected outcomes were compared to those derived from conventional modeling techniques. The results indicate that for the employed experimental configuration and a limited number of features, artificial intelligence technologies, specifically regression-based neural networks can model the recovery rate (or extraction degree) of the ISL process for mineral production, achieving a high degree of accuracy compared to traditional CFD and mass transport models.

Multicomponent and Surface Charge Effects on PFOS Sorption and Transport in Goethite-Coated Porous Media under Variable Hydrochemical Conditions.

Perfluorooctanesulfonate (PFOS), a toxic anionic perfluorinated surfactant, exhibits variable electrostatic adsorption mechanisms on charge-regulated minerals depending on solution hydrochemistry. This work explores the interplay of multicomponent interactions and surface charge effects on PFOS adsorption to goethite surfaces under flow-through conditions. We conducted a series of column experiments in saturated goethite-coated porous media subjected to dynamic hydrochemical conditions triggered by step changes in the electrolyte concentration of the injected solutions. Measurements of pH and PFOS breakthrough curves at the outlet allowed tracking the propagation of multicomponent reactive fronts. We performed process-based reactive transport simulations incorporating a mechanistic network of surface complexation reactions to quantitatively interpret the geochemical processes. The experimental and modeling outcomes reveal that the coupled spatio-temporal evolution of pH and electrolyte fronts, driven by the electrostatic properties of the mineral, exerts a key control on PFOS mobility by determining its adsorption and speciation reactions on goethite surfaces. These results illuminate the important influence of multicomponent transport processes and surface charge effects on PFOS mobility, emphasizing the need for mechanistic adsorption models in reactive transport simulations of ionizable PFAS compounds to determine their environmental fate and to perform accurate risk assessment.

A coupled three-dimensional wormhole modeling in heterogenous carbonate lithology in limited-entry and openhole completions

Wormhole propagation in carbonate formation is a well-researched area with several proposed empirical, analytical, and numerical models to predict the process efficiency. The most reliable and up-to-date model is the two-scale continuum (TSC) model, based on numerical simulation of reactive transport in a porous medium. The model has been utilized in two and occasionally in three-dimensional space but has yet to consider the lithology impact in three-dimensional space (3D). This study demonstrated the impact of lithology considering both openhole and limited-entry completion in 3D space. Moreover, the model considered the acid temperature impact on acidizing efficiency. Results showed that limited entry results in a longer wormhole at the expense of its coverage along the wellbore. Similarly, the higher the number of perforations, the lower the wormhole radius but the higher the coverage. Increasing acid temperature results in better dolomite and lower limestone acid stimulation efficiency. In mixed mineralogy, the positive impact of temperature appears at 50 °C and higher. Wormholes propagate in the limestone sections in layered formation with openhole completion, leaving the dolomite under-stimulated, even at high acid temperatures. Limited entry completion in dolomite layers did not prevent the acid from traveling to the limestone layers but provided limited stimulation in the dolomite section. This is the first study to utilize the TSC model to predict the acid stimulation outcomes at field conditions in 3D space.

Recent Advances in Geochemical and Mineralogical Studies on CO2–Brine–Rock Interaction for CO2 Sequestration: Laboratory and Simulation Studies

The urgent need to find mitigating pathways for limiting world CO2 emissions to net zero by 2050 has led to intense research on CO2 sequestration in deep saline reservoirs. This paper reviews key advancements in lab- and simulation-scale research on petrophysical, geochemical, and mineralogical changes during CO2–brine–rock interactions performed in the last 25 years. It delves into CO2 MPD (mineralization, precipitation, and dissolution) and explores alterations in petrophysical properties during core flooding and in static batch reactors. These properties include changes in wettability, CO2 and brine interfacial tension, diffusion, dispersion, CO2 storage capacity, and CO2 leakage in caprock and sedimentary rocks under reservoir conditions. The injection of supercritical CO2 into deep saline aquifers can lead to unforeseen geochemical and mineralogical changes, possibly jeopardizing the CCS (carbon capture and storage) process. There is a general lack of understanding of the reservoir’s interaction with the CO2 phase at the pore/grain scale. This research addresses the gap in predicting the long-term changes of the CO2–brine–rock interaction using various geochemical reactive transport simulators. Péclet and Damköhler numbers can contribute to a better understanding of geochemical interactions and reactive transport processes. Additionally, the dielectric constant requires further investigation, particularly for pre- and post-CO2–brine–rock interactions. For comprehensive modeling of CO2 storage over various timescales, the geochemical modeling software called the Geochemist’s Workbench was found to outperform others. Wettability alteration is another crucial aspect affecting CO2–brine–rock interactions under varying temperature, pressure, and salinity conditions, which is essential for ensuring long-term CO2 storage security and monitoring. Moreover, dual-energy CT scanning can provide deeper insights into geochemical interactions and their complexities.

Fluid inertia controls mineral precipitation and clogging in pore to network-scale flows

Mineral precipitation caused by fluid mixing presents complex control and predictability challenges in a variety of natural and engineering processes, including carbon mineralization, geothermal energy, and microfluidics. Precipitation dynamics, particularly under the influence of fluid flow, remain poorly understood. Combining microfluidic experiments and three-dimensional reactive transport simulations, we demonstrate that fluid inertia controls mineral precipitation and clogging at flow intersections, even in laminar flows. We observe distinct precipitation regimes as a function of Reynolds number (Re). At low Reynolds numbers (Re < 10), precipitates form a thin, dense layer along the mixing interface, which shuts precipitation off, while at high Reynolds numbers (Re > 50), strong three-dimensional flows significantly enhance precipitation over the entire intersection, resulting in rapid clogging. When injection rates from two inlets are uneven, flow symmetry-breaking leads to unexpected flow bifurcation phenomena, which result in enhanced concurrent precipitation in both downstream channels. Finally, we extend our findings to rough channel networks and demonstrate that the identified inertial effects on precipitation at the intersection scale are also present and even more dramatic at the network scale. This study sheds light on the fundamental mechanisms underlying mixing-induced mineral precipitation and provides a framework for designing and optimizing processes involving mineral precipitation.

A multiphysics coupling framework for exascale simulation of fracture evolution in subsurface energy applications

Predicting the evolution of fractured media is challenging due to coupled thermal, hydrological, chemical and mechanical processes that occur over a broad range of spatial scales, from the microscopic pore scale to field scale. We present a software framework and scientific workflow that couples the pore scale flow and reactive transport simulator Chombo-Crunch with the field scale geomechanics solver in GEOS to simulate fracture evolution in subsurface fluid-rock systems. This new multiphysics coupling capability comprises several novel features. An HDF5 data schema for coupling fracture positions between the two codes is employed and leverages the coarse resolution of the GEOS mechanics solver which limits the size of data coupled, and is, thus, not taxed by data resulting from the high resolution pore scale Chombo-Crunch solver. The coupling framework requires tracking of both before and after coarse nodal positions in GEOS as well as the resolved embedded boundary in Chombo-Crunch. We accomplished this by developing an approach to geometry generation that tracks the fracture interface between the two different methodologies. The GEOS quadrilateral mesh is converted to triangles which are organized into bins and an accessible tree structure; the nodes are then mapped to the Chombo representation using a continuous signed distance function that determines locations inside, on and outside of the fracture boundary. The GEOS positions are retained in memory on the Chombo-Crunch side of the coupling. The time stepping cadence for coupled multiphysics processes of flow, transport, reactions and mechanics is stable and demonstrates temporal reach to experimental time scales. The approach is validated by demonstration of 9 days of simulated time of a core flood experiment with fracture aperture evolution due to invasion of carbonated brine in wellbore-cement and sandstone. We also demonstrate usage of exascale computing resources by simulating a high resolution version of the validation problem on OLCF Frontier.

Quartz Dissolution Effects on Flow Channelization and Transport Behavior in Three‐Dimensional Fracture Networks

AbstractWe 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.

Accelerating CO2 Storage Site Characterization through a New Understanding of Favorable Formation Properties and the Impact of Core-Scale Heterogeneities

CO2 sequestration in deep geologic formations can permanently reduce atmospheric CO2 emissions and help to abate climate change. Target formations must undergo a time- and resource-intensive site evaluation process, assessing storage capacity, environmental safety, and suitability for CO2 trapping via reactive transport models based on data from a limited number of core samples. As such, simulations are often simplified and omit heterogeneities in formation properties that may be significant but are not well understood. To facilitate more rapid site assessment, this work first defines the aquifer properties of favorable storage formations through the analysis of promising and active storage sites. Data show quartz is the most prevalent formation mineral with carbonate minerals, highly reactive with injected CO2, present in over 75% of formations. Porosity and permeability data are highly clustered at 10–30% and 10–1000 mD. Field-scale reactive transport simulations are then constructed and used to analyze CO2 trapping efficiency. The models consider porosity and carbonate mineral heterogeneity as well as the impacts of typical temperature gradients. Simulated sequestration efficiencies are compared to results from a comparable homogenous model to understand the implications of aquifer non-uniformities. The results show a lower sequestration efficiency in the homogeneous model during the injection phase. During the post-injection phase, the homogenization of porosity and carbonate mineralogy results in a higher sequestration efficiency. Incorporating the temperature gradient also increases the sequestration efficiency. Importantly, the maximum deviation between the homogeneous and heterogeneous simulations at the end of the 50-year study period is only ~10%. Larger impacts may be incurred for properties outside the defined, promising ranges suggested here.

Stochastic 1-D reactive transport simulations to assess silica and carbonate phases during the CO2 reinjection process in metasediments

Stochastic 1-D reactive transport simulations to assess silica and carbonate phases during the CO2 reinjection process in metasediments

Tellurium Transport and Enrichment in Volcanogenic Massive Sulfide Deposits: Numerical Simulations of Vent Fluids and Comparison to Modern Sea-Floor Sulfides

Abstract Volcanogenic massive sulfide deposits may represent a significant future source of Te, which is a critical element important for the green energy transition. Tellurium is enriched in these settings by up to 10,000 times over its crustal abundance, indicating that fluids in sea-floor hydrothermal systems may transport and precipitate Te. The major element composition of these hydrothermal fluids is controlled by fluid-rock interaction and is well documented based on experimental, modeling, and natural studies; however, controls on Te mobility are still unknown. To better understand Te enrichment in this deposit type, numerical simulations of the mafic-hosted Vienna Woods and the felsic-hosted Fenway sea-floor vents in the Manus basin were performed to predict Te mobility in modern sea-floor hydrothermal vent fluids and Te deposition during sulfide formation. These simulations demonstrate that the mobility of Te in sea-floor hydrothermal systems is primarily controlled by fluid redox and temperature. Tellurium mobility is low in reduced hydrothermal fluids, whereas mobility of this metal is high at oxidized conditions at temperatures above 250°C. Numerical simulations of the reduced vent fluids of the mafic-hosted Vienna Woods site at the back-arc spreading center in the Manus basin yielded Te concentrations as low as 0.2 ppt. In contrast, the more oxidized model fluids of the felsic-hosted Fenway site located on Pual Ridge in the eastern Manus basin contain 50 ppt Te. The models suggest that Te enrichment in these systems reflects rock-buffer control on oxygen fugacity, rather than an enriched source of Te. In fact, the mafic volcanic rocks probably contain more Te than felsic volcanic rocks. The association of elevated Te contents in the felsic-hosted Fenway system likely reflects magmatic volatile input resulting in lower pH and higher Eh of the fluids. More generally, analysis of sulfide samples collected from modern sea-floor vent sites confirms that redox buffering by the host rocks is a first-order control on Te mobility in hydrothermal fluids. The Te content of sulfides from sea-floor hydrothermal vents hosted by basalt-dominated host rocks is generally lower than those of sulfides from vents located in felsic volcanic successions. Literature review suggests that this relationship also holds true for volcanogenic massive sulfides hosted in ancient volcanic successions. Results from reactive transport simulations further suggest that Te deposition during sulfide formation is primarily temperature controlled. Modeling shows that tellurium minerals are coprecipitated with other sulfides at high temperatures (275°–350°C), whereas Te deposition is distinctly lower at intermediate (150°–275°C) and low temperatures (100°–150°C). These predictions agree with geochemical analyses of sea-floor sulfides as Te broadly correlates positively with Cu and Au enrichment in felsic-hosted systems. The findings of this study provide an important baseline for future studies on the behavior of Te in hydrothermal systems and the processes controlling enrichment of this critical mineral in polymetallic sulfide ores.

Study on Numerical Simulation of Reactive-Transport of Groundwater Pollutants Caused by Acid Leaching of Uranium: A Case Study in Bayan-Uul Area, Northern China

Acid in situ leaching (ISL) is a common approach to the recovery of uranium in the subsurface. In acid ISL, there are numerous of chemical reactions among the injected sulfuric acid, groundwater, and porous media containing ore layers. A substantial amount of radioactive elements including U, Ra, Rn, as well as conventional elements like K, Na, and Ca, and trace elements such as As, Cd, and Pb, are released into the groundwater. Thus, in acid ISL, understanding the transport and reactions of these substances and managing pollution control is crucial. In this study, a three-dimensional reactive transport modeling (RTM) using TOUGHREACT was built to investigate the dynamic reactive migration process of UO22+, H+, and SO42− at a typical uranium mine of Bayan-Uul. The model considering the partial penetration through wellbore in confined aquifer and complex chemical reactions among main minerals like uranium, K-feldspar, calcite, dolomite, anhydrite, gypsum, iron minerals, clay minerals, and other secondary minerals. The results show that after mining for one year, from the injection well to the extraction well, the spatial distribution of uranium volume fraction does not consistently increase or decrease, but it decreases initially and then increases. After mining for one year, the concentration front of UO22+ is about 20 m outside the mining area, the high concentration zone is mainly inside the mining area. The concentration front of H+ is no more than 50 m. SO42− is the index with the highest concentration among the three indexes, the concentration front of SO42− is no more than 100 m. The concentration breakthrough curve of the observation well 10 m from the mining area indicates that the concentrations of the three indicators began to significantly rise approximately after mining 0.05 years, reached the maximum value after mining 0.08 to 0.1 years, and then stabilized. The parameter sensitivity of absolute permeability and specific surface area of minerals shows that the concentration of H+ and SO42− is positively correlated with absolute permeability. The concentration of H+ is negatively correlated with the specific surface area of calcite, anhydrite, K-feldspar, gypsum, hematite, and dolomite. The concentration of SO42− is positively correlated with the specific surface area of K-feldspar and Hematite, and negatively correlated with the specific surface area of calcite, anhydrite, gypsum, and dolomite. The influence analysis of pumping ratio and non-uniform injection ratio shows that the non-uniform injection scheme has a more significant impact on pollution control. The water table, streamline, capture envelope, and the concentration breakthrough curve of five schemes with different pumping ratios and non-uniform injection ratio were obtained. The water table characteristics of five schemes shown that increase in the pumping ratio and the non-uniform injection ratio, the water table convex near the outer injection well is weakened and the groundwater depression cone near the pumping well is strengthened. This characteristic of water table exerts a notable retarding influence on the migration of pollutants from the mining area to the outside. For the scheme with a pumping ratio is 0 (the total pumping flow rate is equal to the total injection flow rate) and a non-uniform injection ratio is 0 (the flow rate of inner injection well Q1,Q2,Q3 is equal to the flow rate of outer injection well Q4,Q5,Q6), the streamline characteristics shown that a segment of the streamline of is diverging from inner region to the outer region. For other schemes, the streamline exhibits a convergent feature. It is indicated that by increasing the pumping ratio and non-uniform injection ratio, a closure flow field can be established, confining the groundwater pollutants resulting from mining within the capture envelope. Hence, the best scheme for preventing pollution migration is the scheme with a pumping ratio is 0 (the total pumping flow rate is equal to the total injection flow rate) and a non-uniform injection ratio is 0.1 (the flow rate of inner injection well Q1,Q2,Q3 is 10% more than the flow rate of outer injection well Q4,Q5,Q6). In this scheme, the optimal stable concentration of UO22+, H+, and SO42− at the observation well obtained by RTM is lower than other schemes, and the values are 0.00316 mol/kg, 2.792 (pH), and 0.0952 mol/kg. The inner well injection rate is 194.09 m3/d, the outer well injection rate is 158.89 m3/d, and the pumping rate is 264.00 m3/d. Numerical simulation analysis suggests that a scheme with a larger non-uniform injection ratio is more conducive to the formation of a strong hydraulic capture zone, thereby controlling the migration of pollutants in the acid ISL. A reasonable suggestion is to adopt non-uniform injection mining mode in acid ISL.

A review of interaction mechanisms and microscopic simulation methods for CO2-water-rock system

This work systematically reviews the complex mechanisms of CO2-water-rock interactions, microscopic simulations of reactive transport (dissolution, precipitation and precipitate migration) in porous media, and microscopic simulations of CO2-water-rock system. The work points out the key issues in current research and provides suggestions for future research. After injection of CO2 into underground reservoirs, not only conventional pressure-driven flow and mass transfer processes occur, but also special physicochemical phenomena like dissolution, precipitation, and precipitate migration. The coupling of these processes causes complex changes in permeability and porosity parameters of the porous media. Pore-scale microscopic flow simulations can provide detailed information within the three-dimensional pore and throat space and explicitly observe changes in the fluid-solid interfaces of porous media during reactions. At present, the research has limitations in the decoupling of complex mechanisms, characterization of differential multi-mineral reactions, precipitation generation mechanisms and characterization (crystal nucleation and mineral detachment), simulation methods for precipitation-fluid interaction, and coupling mechanisms of multiple physicochemical processes. In future studies, it is essential to innovate experimental methods to decouple “dissolution–precipitation–precipitate migration” processes, improve the accuracy of experimental testing of minerals geochemical reaction-related parameters, build reliable characterization of various precipitation types, establish precipitation-fluid interaction simulation methods, coordinate the boundary conditions of different physicochemical processes, and, finally, achieve coupled flow simulation of “dissolution−precipitation−precipitate migration” within CO2-water-rock systems.

P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments

Abstract. The porous microenvironment of soil offers various environmental functions which are governed by physical and reactive processes. Understanding reactive transport processes in porous media is essential for many natural systems (soils, aquifers, aquatic sediments or subsurface reservoirs) or technological processes (water treatment or ceramic and fuel cell technologies). In particular, in the vadose zone of the terrestrial subsurface the spatially and temporally varying saturation of the aqueous and the gas phase leads to systems that involve complex flow and transport processes as well as reactive transformations of chemical compounds in the porous material. To describe these interacting processes and their dynamics at the pore scale requires a well-suited modelling framework accounting for the proper description of all relevant processes at a high spatial resolution. Here we present P3D-BRNS as a new open-source modelling toolbox harnessing the core libraries of OpenFOAM and coupled externally to the Biogeochemical Reaction Network Simulator (BRNS). The native OpenFOAM volume-of-fluid solver is extended to have an improved representation of the fluid–fluid interface. The solvers are further developed to couple the reaction module which can be tailored for a specific reactive transport simulation. P3D-RBNS is benchmarked against three different flow and reactive transport processes: (1) fluid–fluid configuration in a capillary corner, (2) mass transfer across the fluid–fluid interface and (3) microbial growth with a high degree of accuracy. Our model allows for simulation of the spatio-temporal distribution of all biochemical species in the porous structure (obtained from μ-CT images), for conditions that are commonly found in the laboratory and environmental systems. With our coupled computational model, we provide a reliable and efficient tool for simulating multiphase, reactive transport in porous media.

Reactive Transport Process of Earthquake‐induced Hydro‐chemical Changes in Guanding Thermal Spring, Western Sichuan, China

AbstractEarthquake‐related hydrochemical changes in thermal springs have been widely observed; however, quantitative modeling of the reactive transport process is absent. In the present study, we apply reactive transport simulation to capture the hydrochemical responses in a thermal spring following the Wenchuan Ms 8.0 and Lushan Ms 7.0 earthquakes. We first constrain deep reservoir geothermal fluid compositions and temperature by multicomponent geothermometry, and then a reactive geochemical transport model is constructed to reproduce the hydrochemical evolution process. The results show that the recharge from the shallow aquifer increases gradually until it reaches a peak because of the permeability enhancement caused by the Lushan earthquake, which may be the mechanism to explain the earthquake‐related hydrochemical responses. In contrast to the postseismic effect of the Wenchuan earthquake, the chemical evolution can be considered as hydrochemical anomalies related to the Lushan earthquake. This study proves that the efficient simulation of reactive transport processes is useful for investigating earthquake‐related signals in hydrochemical time series.

Integrated surrogate framework for reactive transport simulation of uranium in situ leaching with generative models

The heterogeneity of uranium-bearing aquifers plays a crucial role in governing the migration and leaching of uranium. Uncertainty quantification (UQ) based on the Monte Carlo method is usually used to deal with the issue of aquifer heterogeneity. However, the substantial computational costs of this method arise from a large number of calls to the uranium reactive transport simulation; therefore, surrogate models are developed to reduce these costs. This study leverages a conditional deep convolutional generative adversarial network (cDC-GAN) and an adversarial autoencoder-based kriging regression (AAE-KRG) to develop an integrated surrogate framework for uranium reactive transport simulation. cDC-GAN and AAE-KRG are designed to achieve the cross-domain mapping of the permeability coefficient field to the uranium spatial distribution and leaching concentration over time, respectively. The results demonstrate a structural similarity exceeding 0.9 between the cDC-GAN and the numerical simulation, with a mean square error generally less than 0.002. These findings suggest that the cDC-GAN exhibits high accuracy and stability in terms of replacing the numerical simulation to obtain the spatial-temporal evolution of the uranium plume. Additionally, cDC-GAN is a dynamic surrogate model with the capability of interpolation because uranium spatial distributions with multiple time steps are used as training data during the training process. AAE-KRG also exhibits high accuracy and stability in terms of predicting the leaching concentration by permeability coefficient fields, as evidenced by determination coefficients generally exceeding 0.95. By comparing the computational costs, it is clear that cDC-GAN and AAE-KRG are suitable for processing spatial distribution and time series data, respectively. The trained surrogate models are used to undertake UQ of the uranium leaching area and leaching concentration. The results indicate that the uncertainty of the leaching area in the field-test model increases with time, and the increasing rate reaches a maximum in the middle period (201–300 d). The uncertainty of the leaching concentration increases with time in the early period, decreases after peaking at 137 d, and stabilizes in the middle and late periods. It is observed that the change characteristics of the uncertainty are consistent with the values of the leaching area or concentration. Furthermore, the spatial distribution pattern of uranium pollution is determined based on stochastic simulation results. The uncertainty intervals and the pollution risk status at multiple time steps are quickly obtained by surrogate models, which is helpful for scheme optimization in uranium mining. Owing to its characteristics of being data-driven and highly adaptable to nonlinear data, the integrated surrogate framework has applications beyond uranium in situ leaching.

Phase-field modeling and effective simulation of non-isothermal reactive transport

We consider single-phase flow with solute transport where ions in the fluid can precipitate and form a mineral, and where the mineral can dissolve and release solute into the fluid. Such a setting includes an evolving interface between fluid and mineral. We approximate the evolving interface with a diffuse interface, which is modeled with an Allen–Cahn equation. We also include effects from temperature such that the reaction rate can depend on temperature, and allow heat conduction through fluid and mineral. As Allen–Cahn is generally not conservative due to curvature-driven motion, we include a reformulation that is conservative. This reformulation includes a non-local term that makes the use of standard Newton iterations for solving the resulting non-linear system of equations very slow. We instead apply L-scheme iterations, which can be proven to converge for any starting guess, although giving only linear convergence. The three coupled equations for diffuse interface, solute transport and heat transport are solved via an iterative coupling scheme. This allows the three equations to be solved more efficiently compared to a monolithic scheme, and only few iterations are needed for high accuracy. Through numerical experiments we highlight the usefulness and efficiency of the suggested numerical scheme and the applicability of the resulting model.

Reactive transport modelling of tailings hydrogeochemistry under a composite cover

Quantitative forecasts of acid mine drainage (AMD) production are important for remediation planning. Reactive transport simulations corresponding to a detailed sampling location at a covered legacy tailings impoundment in northern Ontario, Canada, were conducted to quantitatively assess the predominant hydrogeochemical reactions. The simulations span the period from the end of tailings deposition (circa 1970) to early 2020, 12 years after remediation by a five-layer composite cover. The conceptual model of uncovered tailings weathering and subsequent geochemistry of the covered tailings system was implemented in 1D using the multi-component reactive transport code MIN3P. Transient monthly infiltration, post-cover boundary condition changes, and a dynamic temperature regime were incorporated. The shrinking core model, including parallel O2(aq) and Fe3+ oxidation reactions for the waste rock in the cover and the underlying tailings, was implemented to simulate the oxidation of As-bearing pyrite, chalcopyrite, and sphalerite. Primary carbonate and aluminosilicate host minerals promoted acid-neutralization reactions. Precipitation of secondary phases and sorption/desorption of Cu, Zn, and arsenite were incorporated into the model. The overall agreement between key simulated and field-measured post-cover aqueous geochemical parameters suggests that the conceptual model captured the primary hydrogeochemical processes in the covered tailings. A lack of reliable data on initial tailings mineralogy and pre-cover hydrogeochemistry increased simulation uncertainty. Simulated reaction rates indicate that where intact, the cover decreased sulfide oxidation rates by both O2(aq) and Fe3+ and improved pore-water quality over time. Simulation results indicate that elevated concentrations of Zn and As are likely to persist in the tailings regardless of cover performance, whereas concentrations of Cu and Al are the parameters most sensitive to cover effectiveness.

Capturing the Dynamic Processes of Porosity Clogging

AbstractUnderstanding mineral precipitation induced porosity clogging and being able to quantify its non‐linear feedback on transport properties is fundamental for predicting the long‐term evolution of energy‐related subsurface systems. Commonly applied porosity‐diffusivity relations used in numerical simulations on the continuum‐scale predict the case of clogging as a final state. However, recent experiments and pore‐scale modeling investigations suggest dissolution‐recrystallization processes causing a non‐negligible inherent diffusivity of newly formed precipitates. To verify these processes, we present a novel microfluidic reactor design that combines time‐lapse optical microscopy and confocal Raman spectroscopy, providing real‐time insights of mineral precipitation induced porosity clogging under purely diffusive transport conditions. Based on 2D optical images, the effective diffusivity was determined as a function of the evolving porous media, using pore‐scale modeling. At the clogged state, Raman isotopic tracer experiments were conducted to visualize the transport of deuterium through the evolving microporosity of the precipitates, demonstrating the non‐final state of clogging. The evolution of the porosity‐diffusivity relationship in response to precipitation reactions shows a behavior deviating from Archie's law. The application of an extended power law improved the description of the evolving porosity‐diffusivity, but still neglected post‐clogging features. Our innovative combination of microfluidic experiments and pore‐scale modeling opens new possibilities to validate and identify relevant pore‐scale processes, providing data for upscaling approaches to derive key relationships for continuum‐scale reactive transport simulations.

Reactive Transport Modelling of carbon mineralization – a machine learning-based approach

Carbon mineralisation is currently one of the most promising Carbon Capture and Storage (CSS) options for permanent gas sequestration since it ensures a rapid conversion of carbon from the gas phase to a carbonate mineral. Mafic and ultramafic rocks are the best geological options for mineral carbonation due to the relatively fast dissolution rates of the mineral components that can release carbonatable divalent cations such as Mg2+, Sr2+, Ba2+, Mn2+, Ca2+ and Fe2+. Once in solution, these cations react with dissolved carbonate ions to form, under convenient pH and temperature conditions, carbonate minerals like calcite, magnesite, siderite, rhodochrosite, or dolomite.&#x0D; The viability of carbon mineralization with injection of freshwater has been proven in the CarbFix project [1]. The main feature of the CarbFix method is that CO2 (and other minor gases) is not injected under its gaseous form but already dissolved in an aqueous fluid, which greatly enhances the mineralisation rates. The methodology, however, requires vast amounts of water that can be a limited resource in many regions. Seawater may be the most viable water source for underground carbonation in many areas, but its use raises questions on the efficiency of the carbonation process. The high ionic strength of seawater makes the geochemical interactions between CO2-rock and seawater more complex and, so far, more unpredictable. Reactive transport modelling (RTM) based on experimental thermodynamic and kinetic data of seawater-basalt interaction [2, 3] can shed some light on the expected mineralisation progress in sites with different mineralogy and variable temperature conditions. Figure 1 shows the results obtained with a reactive transport model that simulates the injection of CO2-charged seawater in basaltic rock.&#x0D; geochemical reaction calculations in particular might be considerably slow and are sometimes redundant as they might be based on a very similar set of input values. These challenges are usually faced by introducing a number of simplifications in the models to meet the objectives of the simulation studies. Thus, alternative ways to solving the full system are a priori a potential way to reduce the computational burden of numerical models.&#x0D; One of such alternatives is Machine Learning (ML) and, more specifically, Artificial Neural Networks (ANN), which are inspired on biological neural networks as the ones found in the human brain. They contain several computational “neurons” arranged in different layers. Each of these neurons can be activated based on the response of the other connected neurons. The training process consists of feeding data into the ANN and fitting the connection weights between the neurons to reproduce and learn the hidden relationships of the data. Recently, the authors have coupled ANNs of a geochemical system into a reactive transport simulator [5]. In this approach, Supervised Machine Learning with ANNs is used to accurately predict the geochemical evolution in a reactive transport framework without the need of actually performing the (expensive) chemical equilibrium calculations in the reactive transport model.&#x0D; The goal of this work is to apply and further develop the technology based on Supervised Machine Learning algorithms to drastically boost the efficiency of reactive transport models of carbon mineralization in the Carbfix system [6]. Firstly, a set of geochemical models of CCS are developed in batch systems (0D) as well as reactive transport models in 1D axisymmetric, 2D, and 3D RTM modelling with different degrees of complexity. Modelling focuses on the injection of CO2-charged seawater into basaltic rocks. In this system, CO2 is consumed via a two-step reaction: (1) Dissolution of Basaltic Glass and release of cations (Ca and Mg), and (2) precipitation of Ca-Mg carbonates and other secondary minerals. The RTM models consider a 1 km domain and a characteristic mineralization of 3-4 years. As a first step, these models are solved with the convential (non ML-based) reactive transport simulator iCP [7]. Then, ANNs are trained and validated with the latest developments in setting up neural networks and used to solve these RTM systems with a Comsol-ML library. The resulting ANNs present good accuracies with coefficients of determination (R2) above 0.95 for all outputs, and a batch calculation speedup of 30 as compared to traditional chemical equilibration with PhreeqC. Preliminary results of RTM using ML to predict the chemical step indicate a speedup of on order of magnitude and reasonable accuracy as compared to conventional RTM. Our results demonstrate that it is conceivable to replace a geochemical solver in a RTM environment with significantly faster surrogate-based models powered by a machine learning algorithm