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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗
DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A parametric study of accelerated carbonation in alkali-activated slag

Resistance to carbonation is one important attribute that low-CO{sub 2} cement alternatives must possess, and is particularly crucial for cement alternatives subjected to aggressive CO{sub 2} concentrations such as those used in construction of oil wells and wells for below ground carbon sequestration. Here, a parametric study of alkali-activated slag (AAS) carbonation in aggressive environments has been conducted to examine (i) calcium carbonate polymorphism using X-ray diffraction (XRD) and Fourier-transform infrared (FTIR) spectroscopy, and (ii) the extent of calcium carbonate formation and CO{sub 2} adsorption using thermogravimetric analysis (TGA). A range of AASs have been studied by varying the magnesium content of the slag, the activator type (sodium hydroxide and sodium silicate), the activator concentration, and the curing time prior to carbonation. It was uncovered that both (i) magnesium from the slag and (ii) silica from the activating solution are needed to reduce the propensity for the sodium-containing calcium-alumino-silicate-hydrate gel to undergo decalcification.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The intrinsic mechanical properties of hydromagnesite, Mg 5 (CO 3 ) 4 (OH) 2 ·4H 2 O, a key phase of reactive MgO carbonate cement

To potentially enable CO 2 sequestration, reactive MgO carbonate cement is emerging as an alternative binder to Portland cement. Understanding the mechanical properties of its binding phase is critical for understanding the strength development and performing materials design for reactive MgO cement systems; however, the intrinsic mechanical properties of hydromagnesite (Mg 5 (CO 3 ) 4 (OH) 2 ·4H 2 O), a key binding phase, remain unexplored. Here the present study utilized synchrotron-based high-pressure X-ray diffraction to determine the unit cell-scale, intrinsic mechanical properties of hydromagnesite for the first time. Up to hydrostatic loading of 7.7 GPa, the bulk modulus of hydromagnesite was determined as 59 GPa or 71 GPa fitted using the second-order or third-order Birch-Murnaghan equation of state, which we contextualize with binding phases in various cement systems. The experiment results are applicable in materials design of low-carbon concrete and valuable for the validation and calibration of atomistic models.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The impact of capillary heterogeneity on CO 2 flow and trapping across scales

Capillary heterogeneity has been identified over the last decade as a key control on subsurface CO 2 flow behavior during geological CO 2 sequestration. These heterogeneities can be formed in all sedimentary rocks, ranging from slight variations in the sand grain sizes to extensive sequences of interbedded sands, shales, and limestones. Capillary heterogeneity has been largely, although not entirely, overlooked in subsurface flow modeling because it is assumed to only directly influence fluid redistribution over scales of centimeters to meters. However, even small-scale fluid movements can result in dramatic impacts on the mobility and trapping of the CO 2 over kilometers. Therefore, neglecting capillary heterogeneity at multiple scales could potentially lead to errors in modeling and predicting field-scale plume migration. In this review paper, we aim to provide a consistent overview to (1) establish that capillary heterogeneity can have a major impact on CO 2 plume migration, (2) establish the respective length scales at which capillary heterogeneity matters, and (3) provide guidance for numerical modeling. This review covers pertinent literature and extracts key observations from the core to the field scales. Experimental studies have shown that millimeter-decimeter scale capillary heterogeneity can cause the so-called capillary heterogeneity trapping in addition to pore-scale residual trapping. Even at such a small scale, capillary heterogeneity can already lead to complex upscaled constitutive relationships, such as flow-rate dependent and anisotropic relative permeability, which affects field-scale CO 2 migration even when field-scale heterogeneities are present. Under gravity-dominated flow regimes, centimeter-meter scale capillary heterogeneity can entrap a significant amount of CO 2 at field scale, not just after imbibition but also during drainage. In certain cases, the presence of capillary heterogeneity can even completely stop the vertical movement of the CO 2 plume, hence greatly reducing leakage risks. At meter-kilometer scale, the influence of capillary heterogeneity is more pronounced and can hinder or redirect CO 2 migration in both lateral and vertical directions. The impact of capillary heterogeneity across multiple spatial scales poses a great challenge in modeling CO 2 migration at field scale, because it is practically impossible to build a field-scale earth model with grid blocks at millimeter scale. We recommend a hierarchical modeling approach to address this challenge. At field scale, earth models are built to capture geological features and heterogeneities in high but still practical grid resolutions. For each facies or rock type of the field-scale model, high- resolution meter-scale “conceptual” models are built with millimeter-scale grid blocks to capture representative fine-scale bedding geometries and heterogeneities in various environments of deposition, bridging the gap from subcore scale to the size of a field-scale simulation grid block. Upscaling is then used to preserve the smaller-scale flow dynamics of various rock types in field-scale simulations. Here, future work is needed to (1) refine, improve, and validate the hierarchical modeling approach; (2) build libraries of fine-scale bedding models for facies in various environments of deposition; (3) quantify multiscale capillary heterogeneity effects under subsurface uncertainties; (4) gain learning from different storage formations; and (5) establish best practices that balance accuracy and computational speed.

Capillary heterogeneity↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2022

In situ inelastic neutron scattering of mixed CH 4 –CO 2 hydrates

An abundant source of CH 4 can be found in natural hydrate deposits. Recent demonstration of CH 4 recovery from hydrates via CO 2 exchange has revealed the potential as a fuel source that also provides a medium for carbon sequestration. It is vital to understand the structural and dynamic impacts of guest variation in CH 4 , CO 2 , and mixed hydrates and link the results to the stability of various deposits in nature, harvesting methane, and sequestering CO 2 . Molecular vibrations are examined in CH 4 , CO 2 , and mixed CH 4 -CO 2 hydrates at 5 and 190 K and Xe hydrates for comparison. Inelastic neutron scattering (INS) is an ideal spectroscopy technique to observe the dynamic modes in the hydrate structure and enclathrated CH 4 , as it is extremely sensitive to 1 H. The presence of CO 2 in hydrates tightens the lattice. It introduces more active librational modes to the host lattice, while hindering the motion of CH 4 in mixed CH 4 -CO 2 hydrate at 5 K. At 190 K, a large broadening of the CH 4 librational modes indicates disorder in the structure leading to dissociation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Techno-economic life cycle assessment of CO 2 -EOR operations towards net negative emissions at farnsworth field unit

Optimizations of CO 2 Water Alternating Gas(WAG)- systems with multi-objectives of incremental recovery and maximization of CO 2 storage are challenging. Here, the incorporation of a total Greenhouse gas (GHG) life cycle assessment is mostly ignored leading to inaccurate estimation of overall net carbon emissions of their operations. In this study, the effect of a total GHG life cycle assessment on a multi-objective CO 2 -WAG optimization with integrated techno-economic assessment (TEA) which factors carbon tax credit is conducted. A life cycle assessment (LCA) was conducted utilizing a 20 -year optimized post history matched data from a high fidelity reservoir simulation model. Using data generated from the optimum result, a techno-economic life cycle analysis was further conducted. The first scenario classified as the base model had an estimated 81% of purchased CO 2 sequestered. The results through a comprehensive techno-economic LCA model yielded a net estimate of 73% of purchased CO 2 . The optimized forecasted model which considered key operational and reservoir factors such as WAG ratio, injection rates and periods, and well specification resulted in an improved sequestration of 92% of purchased CO 2 . However, this also dropped to 84% after taking it through LCA. These results clearly indicate a significant amount of net CO 2 is not accounted for when operations are not analyzed through LCA. From the LCA, direct flaring volumes of CO 2 , energy consumption and efficiency of unit equipment were noticed to be the major causes of these reductions. Considering ten main sources of energy as source of energy generation, a comparative techno-eco LCA was conducted. The results confirmed a lower net volume and NPV for energy sources with higher carbon footprints and vice versa. Thus, a total LCA of CO 2 -WAG greatly influences net storage factor of purchased CO 2 and hence project NPV where tax credit/incentives per ton of CO 2 sequestered is considered. Although operational conditions are optimized for best results, there are significant factors that leads to minimization of net storage factor. This study therefore provides an insightful information for optimizing CO 2 -WAG multi-objectives to achieve minimum GHG emission.

02 PETROLEUM↗
DOE OSTI2020

CO 2 plume evolution in a depleted natural gas reservoir: Modeling of conformance uncertainty reduction over time

Uncertainty in the long-term fate of CO 2 injected for geologic carbon sequestration (GCS) is a significant barrier to the adoption of GCS as a greenhouse-gas emission-mitigation for industry and regulatory agencies alike. Here, we present a modeling study that demonstrates that the uncertainty in forecasts of GCS site performance decreases over time as monitoring data are used to update operational models. We consider a case study of GCS in a depleted natural gas reservoir, with CO 2 injection occurring over 20 years, with a 50-year post-injection site care period. We constructed a detailed model to generate the actual model output, which is considered synthetic observation data. A series of simpler operational models based on limited data and assumptions about how an operator would model such a site are then run and compared against actual model output at specific monitoring points after one year, two years, etc. The operational model is updated and improved using the synthetic observation data from the actual model at the same time intervals. Model parameter values and model features needed to be updated over time to improve matches to the actual model. These kinds of model adjustments would be a normal part of reservoir engineering and site management at GCS sites. Uncertainty in two key measures related to site performance decreases with time: extent of the CO 2 plume up-dip migration, and radial extent of the pressure pulse. This conclusion should help allay the concerns of industry and regulators about uncertainty in long-term fate of CO 2 at GCS sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Tracking natural CO 2 migration through a sandstone aquifer using Sr, U and C isotopes: Chimayó, New Mexico, USA

The geochemical and isotopic characteristics of groundwaters in Chimayó, New Mexico, reflect processes that affect water quality in the Tesuque Aquifer, which overlies a leaking natural CO 2 source in a structurally complex region. In this study, select isotopes (δ 13 C, 87 Sr/ 86 Sr, 234 U/ 238 U) are applied to groundwaters to better understand CO 2 transport mechanisms and related water-rock interactions that impact Chimayó groundwater. Carbon stable isotope ratios of dissolved inorganic carbon (DIC; δ 13 CDIC = -15.10‰ to 4.50‰) identify a distinct source of upward-migrating CO 2 that interacts with the groundwater. Additionally, groundwater 87 Sr/ 86 Sr compositions (0.7098 to 0.7154) reflect intrusion of varying amounts of saline water associated with the high CO 2 source, while 234 U/ 238 U ratios corroborate presence of deep groundwater and suggest impacts from distinctive natural uranium sources are affecting groundwater. Previous work proposed two CO 2 transport mechanisms at the site: (1) dissolved in deep brine that underlies the aquifer and (2) in the gas phase; this study uses isotope mixing models to identify wells that are affected by these CO 2 transport mechanisms and demonstrates that both transport mechanisms are associated with impaired groundwaters. Overall, this study demonstrates that applying multiple isotope systems (δ 13 CDIC, 87 Sr/ 86 Sr, 234 U/ 238 U) is a dynamic tool for identifying and measuring the impact of CO 2 leakage from a sequestration site.

58 GEOSCIENCES↗
DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

GCS site selection in saline Miocene formations in South Louisiana

One of the key requirements of a commercial scale geologic carbon sequestration (GCS) site selection process is identifying injection sites to safely inject and retain injected CO 2 over extended time scales. This requires thorough evaluation of site-specific surface and subsurface features. A site screening and selection framework was developed and used to identify potential sites in South Louisiana, incorporating surface data including site proximity to large CO 2 stationary sources, vicinity of large population centres and state lands, land usage, legacy oil and gas wells, and local/regional geologic trends. Regionally the Miocene sediments thicken and dip towards the south, reflecting a prograding deltaic environment during the Miocene which resulted in the deposition of thick packages of fine-grained sandstones suitable for CO 2 storage. The results of three example selected sites that are spatially separated with varying subsurface and surface features are presented in this study. The multiple stacked sand zones at these sites provide ample amount of pore volume for CO 2 storage with varying degree of stratigraphic/structural traps for long terms storage containment. Here, the screening framework presented in this study and the detailed evaluation of three specific sites in South Louisiana provides a process to screen and characterize sites for GCS projects and the suitability of these Miocene sediments for commercial scale CO 2 storage projects. It is envisioned that the analysis described in this paper will be beneficial for a large number of audiences involved in GCS work and aid in the site selection process.

58 GEOSCIENCES↗
DOE OSTI2024

Impacts of irregularly-distributed acidified brine flow on geo-chemo-mechanical alteration in an artificial shale fracture under differential stress

The efficacy of geological carbon sequestration is reliant on the integrity of the caprock and its resistance to physical and chemical alteration. Caprocks with high abundance of reactive carbonates like calcite are susceptible to acid-promoted dissolution and can result in structural weakening. This work investigates the effect of acidified brine flow through an artificially fractured, high-carbonate (30 % by XRD) shale under differential compressive stress. Cylindrical samples were cut in half vertically and milled to create an artificial fracture with interlocking asperities and open channels. Samples were sheared with a single applied stress in a custom flow cell housed within an industrial CT scanner. Further, either acidic (pH 4) or reservoir-simulated (pH 9.5) brine was flowed through the artificial fracture for 7–8 days under reservoir pressure and room temperature. Model simulations indicate flow mainly occurred in open channels, with limited flow between overlapping asperities. Analysis of fracture surfaces by optical and scanning electron microscopy show increased surface alteration and roughness after exposure to pH 4 versus pH 9.5 brine indicating mineral dissolution/loss, and this effect is greater in areas that receive the highest brine flows. Similarly, surface analysis by scratch testing shows fracture toughness decreases more after exposure to acidic versus reservoir-simulated brine, with the greatest alteration in areas of highest acidic brine flows. Despite weakening, no shear slip occurred. Overall, the results indicate that acidified brine can result in significant physical and geomechanical alteration of irregular fracture surfaces in shale caprock, with greatest effects in preferential flow regions.

58 GEOSCIENCES↗
DOE OSTI2022

Radiocarbon analysis as a method for verifying atmospheric CO 2 uptake during carbon mineralization

Sequestration of atmospheric CO 2 is required to constrain the warming of Earth’s climate. Carbon mineralization refers to the precipitation of carbonate minerals by reaction of CO 2 and Mg- and Ca-rich rocks; if the source of the CO 2 is atmospheric and the precipitated carbonate is protected from subsequent dissolution, this process at sufficient scale may be able to reliably draw down atmospheric greenhouse gas levels. Such reactions occur passively in ultramafic settings and previous work has demonstrated that they actively draw down local CO 2 (g) concentrations. However, a method for unambiguously attributing the sequestered carbon solid product to atmospheric sources is still lacking. Here, we test if radiocarbon can be used to verify that the carbon being incorporated into carbonate minerals during carbon mineralization is atmospheric in origin. Additionally, samples from recently exposed ultramafic sites are analyzed and the results demonstrate the utility of this technique for verifying true atmospheric carbon reduction.

58 GEOSCIENCES↗
DOE OSTI2023

Observed impacts of large wind farms on grassland carbon cycling

Deployment of wind energy is an essential renewable energy source that mitigates climate change and reduces air pollution. Over the last several decades, wind energy development has increased worldwide, expanding from ~20 to ~900 GW (gigawatt) during 2001-2022. Nonetheless, researchers have identified unintended consequences of wind energy on microclimate via turbine-altered surface-atmosphere exchanges of energy, momentum, mass, and trace gases. Based on multi-source observations and models, researchers also have drawn some conclusions that wind farms could warm the land surface, especially at night, at regional and continental scales. Consequently, altered microclimates at wind farms may affect vegetation productivity and carbon sequestration, two critically important ecosystem services related to carbon dynamics; however, such potential impacts and driving mechanisms remain poorly understood. Wind energy deployment is increasing globally to meet carbon neutrality goals, with upscaling of onshore wind power capacity projected to grow from 542 GW in 2018 to 1787 and 5044 GW by 2030 and 2050, respectively. Furthermore, increased demand for wind energy deployment may lead to much larger wind farms in open, expansive landscapes. In turn, a large array of geographically clustered wind turbines could collectively modify local microclimate and amplify turbine-atmosphere interactions, which, if large enough, may produce detectable impacts on ecosystem dynamics. Thus, identifying and quantifying the potential impacts of wind farms on carbon-related ecosystem services may facilitate sustainable wind energy development globally.

17 WIND ENERGY↗
DOE OSTI2022

Underground sources of drinking water chemistry changes in response to potential CO 2 leakage

The purpose of this study was to quantify changes to underground sources of drinking water (USDW) quality in response to potential CO 2 leakage from geologic CO 2 sequestration (GCS) reservoirs. Here, the developed a framework of combined laboratory experiments and reactive transport simulations and used this framework to evaluate the Ogallala aquifer overlying the Farnsworth Unit (FWU), an active GCS site, as a case study. Using chemical reaction parameters obtained from laboratory experiments and numerical simulations, site-specific mechanisms of CO 2 -water-sediment interactions at the USDW aquifer were interpreted. Long-term risks of potential CO 2 leakage were then evaluated with field-scale numerical models using the regional hydrogeological characteristics and reaction parameters obtained from our experiments and simulations. Results suggest that carbonate mineral impurity and cation exchange are key mechanisms for interactions between CO 2 and the aquifer sediment. Additionally, for a large leakage rate of 0.1 % injection from one leaky well, the leakage plume might impact an area of 300 m in diameter and significantly affect the local water quality by changing pH and cation concentrations (e.g., Zn, Ba and Sr). After leakage ceases, the zone of impacted fluids would not migrate significantly in subsequent decades due to a low regional groundwater flowrate (for this case study). The relatively small area of impact might not be detected in a monitoring well given the broader spacing in a typical field scenario. Effective early leakage detection may require additional tools, e.g., borehole CO 2 movement, four-dimensional seismicity, CO 2 soil flux, samples from deeper aquifers, etc., to ensure effective leakage detection and long-term safety of GCS projects.

58 GEOSCIENCES↗
DOE OSTI2024

Necromass responses to warming: A faster microbial turnover in favor of soil carbon stabilisation

Microbial byproducts and residues (hereafter ‘necromass’) potentially play the most critical role in soil organic carbon (SOC) sequestration. However, little is known about the influence of climate warming on necromass accumulation in the agroecosystem and the underlying mechanisms associated with microbial life strategies. Here, in order to address these knowledge gaps, we used amino sugars as biomarkers of microbial necromass, and investigated their variation through an 8-year trial in an agroecosystem with two warming levels (+1.6 and + 3.2 °C) compared to ambient temperature. The results showed that the lower warming level had no impact on total microbial necromass carbon. Conversely, warming the soil 3.2 °C above ambient increased total microbial necromass by 17 % and its contribution to SOC by 21.3 %, mainly by increasing fungal necromass (+19.8 %), whereas +3.2 °C warming had no impact on bacterial necromass. At the phylum level, compared with the ambient control, +3.2 °C warming induced an increase in the abundance of Proteobacteria and a decrease in both Acidobacteria and Actinobacteria, whereas in the fungal community, Ascomycota increased and Mortierellomycota decreased. This indicates that r-strategists outcompete K-strategists in warmer climates, which led to increased microbial necromass production and accumulation, as supported by the positive correlation between r-strategists and microbial necromass. Stronger microbial competition for resources also resulted in a higher biomass turnover rate, greater cell death, and greater production of microbial necromass. This was supported by the lower bacterial and fungal network complexity and trophic links under warming conditions. In addition, the necromass generated from accelerated microbial turnover further offsets warming-induced deceases in microbial biomass. Consequently, bulk SOC did not change, despite microbial necromass having a much greater response to warming than the soil C pool. Therefore, future climate warming may influence the composition and persistence of SOC during microbial degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Carbon-Negative Biofuel Production

Achievement of the 1.5 °C limit for global temperature increase relies on the large-scale deployment of carbon dioxide removal (CDR) technologies. In this article, we explore two CDR technologies: soil carbon sequestration (SCS), and carbon capture and storage (CCS) integrated with cellulosic biofuel production. These CDR technologies are applied as part of decentralized biorefinery systems processing corn stover and unfertilized switchgrass grown in riparian zones in the Midwestern United States. Cover crops grown on corn-producing lands are chosen from the SCS approach, and biogenic CO 2 in biorefineries is captured, transported by pipeline, and injected into saline aquifers. The decentralized biorefinery system using SCS, CCS, or both can produce carbon-negative cellulosic biofuels (≤-22.2 gCO 2 MJ –1 ). Meanwhile, biofuel selling prices increase by 15–45% due to CDR costs. Economic incentives (e.g., cover crop incentives and/or a CO 2 tax credit) can mitigate price increases caused by CDR technologies. Lastly, a combination of different CDR technologies in decentralized biorefinery systems is the most efficient method for greenhouse gas (GHG) mitigation, and its total GHG mitigation potential in the Midwest is 0.16 GtCO 2 year –1 .

09 BIOMASS FUELS↗