reactive transport model
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2021 ◽  
Vol 214 ◽  
pp. 106274
Author(s):  
Andreas Jenni ◽  
Johannes C.L. Meeussen ◽  
Tapani A. Pakkanen ◽  
Janne T. Hirvi ◽  
Bukunmi Akinwunmi ◽  
...  

2021 ◽  
Author(s):  
Ahmed M. S. Elgendy ◽  
Simone Ricci ◽  
Elena I. Cojocariu ◽  
Claudio Geloni

Abstract Dynamic-geochemical model is a powerful instrument to evaluate the geochemical effects on CO2storage capacity, injectivity and long-term containment. The study objective is to apply an integrated multi-step workflow to a carbon capture and storage (CCS) candidate field (offshore), namely hereinafter H field. From experimental analyses, a comprehensive real data-tailored reactive transport model (RTM) has been built to capture the dynamics and the geochemical phenomena (e.g., water vaporization, CO2solubility, mineral alteration) occurring during and after the CO2injection in sedimentary formations. The proposed integrated workflow couples lab activities and numerical simulations and it is developed according to the following steps: Mineralogical-chemical characterization (XRD, XRF and SEM-EDX experimental techniques) of field core samples; Data elaboration and integration to define the conceptual geochemical model; Synthetic brine reconstruction by means of 0D geochemical models; Numerical geochemical modelling at different complexity levels. Field rocks chosen for CO2injection have been experimentally characterized, showing a high content of Fe in clayey, micaceous and carbonate mineralogical phases. New-defined, site-specific minerals have been characterized, starting from real XRD, XRF and SEM-EDX data and by calculation of their thermochemical parameters with a proprietary procedure. They are used to reconstruct synthetic formation water chemical composition (at equilibrium with both rock mineralogy and gas phase), subsequently used in RTM. CO2injection is simulated using 2D radial reactive transport model(s) built in a commercial compositional reservoir simulator. The simulations follow a step-increase in the complexity of the model by adding CO2solubility, water vaporization and geochemical reactions. Geochemical processes impact on CO2storage capacity and injectivity is quantitatively analyzed. The results show that neglecting the CO2solubility in formation water may underestimate the max CO2storage capacity in H field by around 1%, maintaining the same pressure build-up profile. Sensitivities on the impact of formation water salinity on the CO2solubility are presented. In a one thousand years’ time-scale, changes in reservoir porosity due to mineral alteration, triggered by CO2-brine-rock interactions, seem to be minimal in the near wellbore and far field. However, it has been seen that water vaporization with the associated halite precipitation inclusion in the simulation models is recommended, especially at high-level of formation brine salinity, for a reliable evaluation of CO2injectivity related risks. The proposed workflow provides a new perspective in geochemical application for CCS studies, which relies on novel labs techniques (analyses automation), data digitalization, unification and integration with a direct connection to the numerical models. The presented procedure can be followed to assess the geochemical short-and long-term risks in carbon storage projects.


2021 ◽  
Author(s):  
Denis Sergeevich Nikolaev ◽  
Nazika Moeininia ◽  
Holger Ott ◽  
Hagen Bueltemeier

Abstract Underground bio-methanation is a promising technology for large-scale renewable energy storage. Additionally, it enables the recycling of CO2 via the generation of "renewable methane" in porous reservoirs using in-situ microbes as bio-catalysts. Potential candidate reservoirs are depleted gas fields or even abandoned gas storages, providing enormous storage capacity to balance seasonal energy supply and demand fluctuations. This paper discusses the underlying bio-methanation process as part of the ongoing research project "Bio-UGS – Biological conversion of carbon dioxide and hydrogen to methane," funded by the German Federal Ministry of Education and Research (BMBF). First, the hydrodynamic processes are assessed, and a review of the related microbial processes is provided. Then, based on exemplary field-scale simulations, the bio-reactive transport process and its consequences for operation are evaluated. The hydrogen conversion process was investigated by numerical simulations on field scale. For this, a two-phase multi-component bio-reactive transport model was implemented by (Hagemann 2018) in the open-source DuMux (Flemisch et al. 2011) simulation toolkit for porous media flow. The underlying processes include the transport of reactants and products, consumption of specific components, and the related growth and decay of the microbial population, resulting in a bio-reactive transport model. The microbial kinetic parameters of methanogenic reactions are taken from the available literature. The simulation study covers different scenarios on conceptional field-scale models, studying the impact of well placement, injection rates, and gas compositions. Due to a significant sensitivity of the simulation results to the bio-conversion kinetics, the field-specific conversion rates must be obtained. Thus, the Bio-UGS project is accompanied by laboratory experiments out of the frame of this paper. Other parameters are rather a matter of design; in the present case of depleted gas fields, those parameters are coupled and can be chosen to convert fully hydrogen and carbon dioxide to methane. Especially the well spacing can be considered the main design parameter in the likely case of a given injection rate and gas composition. This study extends the application of the previously developed code from a homogeneous-2D to the heterogeneous-3D case. The simulations mimic the co-injection of carbon dioxide and hydrogen from a 40 MW electrolysis.


2021 ◽  
Vol 11 (19) ◽  
pp. 9314
Author(s):  
Svenja Steding ◽  
Thomas Kempka ◽  
Michael Kühn

Potash seams are a valuable resource containing several economically interesting, but also highly soluble minerals. In the presence of water, uncontrolled leaching can occur, endangering subsurface mining operations. In the present study, the influence of insoluble inclusions and intersecting layers on leaching zone evolution was examined by means of a reactive transport model. For that purpose, a scenario analysis was carried out, considering different rock distributions within a carnallite-bearing potash seam. The results show that reaction-dominated systems are not affected by heterogeneities at all, whereas transport-dominated systems exhibit a faster advance in homogeneous rock compositions. In return, the ratio of permeated rock in vertical direction is higher in heterogeneous systems. Literature data indicate that most natural potash systems are transport-dominated. Accordingly, insoluble inclusions and intersecting layers can usually be seen as beneficial with regard to reducing hazard potential as long as the mechanical stability of leaching zones is maintained. Thereby, the distribution of insoluble areas is of minor impact unless an inclined, intersecting layer occurs that accelerates leaching zone growth in one direction. Moreover, it is found that the saturation dependency of dissolution rates increases the growth rate in the long term, and therefore must be considered in risk assessments.


Author(s):  
Nicholas Thiros ◽  
W. Gardner ◽  
Marco Maneta ◽  
Douglas Brinkerhoff

We combine physics-based groundwater reactive transport modeling with machine learning techniques to quantify hydrogeologic model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input-output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeologic parameters and hydraulic forcing conditions using Markov-chain Monte Carlo (MCMC) calibration against field observations of groundwater hydraulic heads and H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated site in Wyoming, USA. Our results show that including H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.


2021 ◽  
Author(s):  
Zexuan Xu ◽  
Rebecca Serata ◽  
Haruko Wainwright ◽  
Miles Denham ◽  
Sergi Molins ◽  
...  

Abstract. Climate resilience is an emerging issue at contaminated sites and hazardous waste sites, since projected climate shifts (e.g., increased/decreased precipitation) and extreme events (e.g., flooding, drought) could affect ongoing remediation or closure strategies. In this study, we develop a reactive transport model (Amanzi) for radionuclides (uranium, tritium, and others) and evaluate how different scenarios under climate change will influence the contaminant plume conditions and groundwater well concentrations. We demonstrate our approach using a two-dimensional reactive transport model for the Savannah River Site F-Area, including mineral reaction and sorption processes. Different recharge scenarios are considered by perturbing the infiltration rate from the base case, as well as considering cap failure and climate projection scenarios. We also evaluate the uranium and nitrate concentration ratios between scenarios and the base case to isolate the sorption effects with changing recharge rates. The modeling results indicate that the competing effects of dilution and remobilization significantly influence pH, thus changing the sorption of uranium. At the maximum concentration on the breakthrough curve, higher aqueous uranium concentration implies that sorption is reduced with lower pH due to remobilization. To better evaluate the climate change impacts in the future, we develop the workflow to include the downscaled CMIP5 (Coupled Model Intercomparison Project) climate projection data in the reactive transport model, and evaluate how residual contamination evolves through 2100 under four climate Representative Concentration Pathway (RCP) scenarios. The integration of climate modeling data and hydro-geochemistry models enables us to quantify the climate change impacts, assess which impacts need to be planned for, and therefore assist climate resiliency efforts and help guide site management.


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