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At least 361 records · Page 20

Mesozoic Deserts and CO2 Storage: The Glen Canyon Group, Utah, USA

For much of the Mesozoic an extensive desert environment extended across the Western Interior of the USA. These desert systems deposited vast dunefields, including one of the largest ergs preserved in the rock record. The Glen Canyon Group in Utah is a Triassic-Jurassic aged aeolian succession, consisting of thick, laterally extensive aeolian dune deposits, interdune lakes and oases, and dryland fluvial systems. These aeolian deposits have been identified as potential CO2 sequestration reservoirs. The Jurassic-aged Navajo Sandstone has been the subject of research into its potential as a CO2 reservoir, as well as naturally occurring CO2 seeps in the Green River area. It has excellent reservoir properties, consisting of thick sandstones with high porosity and permeability, and has industry data including well logs and core. It occurs in both outcrop and subcrop, allowing for comparison and sense checking of interpretations across small and large scales. The Triassic Wingate Sandstone has received much less attention, however it too is composed of thick aeolian dune deposits. Like the Navajo Sandstone, the Wingate Sandstone has industry data, and occurs in both outcrop and subcrop. Using extensive industry data which exists across Utah is an effective way to pivot from a hydrocarbon focus to CO2 injection, and so contribute to green energy and carbon neutral emission goals. Here we use historic industry data in conjunction with field work, to study the Navajo and Wingate Sandstones and their potential as CO2 reservoirs, in addition to increasing our understanding of lithologic and stratigraphic complexity in one of the most significant aeolian systems in the world.

Mahon, Elizabeth↗

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗

Bioenergy for climate change mitigation: Scale and sustainability

Many global climate change mitigation pathways presented in IPCC assessment reports rely heavily on the deployment of biomass for bioenergy, often used in conjunction with carbon capture and storage (BECCS). We review the literature on bioenergy, including the modelling of bioenergy in integrated assessment models (IAMs) and bottom-up modelling and non-modelling studies on the implications of bioenergy use. We summarise the limitations of existing modelling studies and what is known about the potential co-benefits and adverse side-effects of bioenergy systems. We find that the implications of bioenergy supply on mitigation and other sustainability criteria are context dependent and influenced by feedstock, management regime, climatic region, scale of deployment and the counterfactual land use and energy system. However, due to limitations of the existing models, and uncertainty over the future context with respect to the many variables that influence availability of biomass and land resources, it is not possible to precisely quantify the sustainability implications for different scales of bioenergy implementation. Given these uncertainties, the dependence on large-scale deployment of bioenergy in mitigation scenarios carries risks. The deployment of bioenergy technologies and the evolution of biomass supply chains at a scale that achieves significant mitigation and carbon sequestration requires integrative policies, coordinated institutions and improved governance mechanisms. As a result, bioenergy, and the use of land to produce biomass, is an essential part of many climate mitigation strategies but there are limits to its use due to trade-offs with sustainability.

09 BIOMASS FUELS↗

New soil carbon sequestration with nitrogen enrichment: a meta-analysis

Background and aims: Through agriculture and industry, humans are increasing the deposition and availability of nitrogen (N) in ecosystems worldwide. Carbon (C) isotope tracers provide useful insights into soil C dynamics, as they allow to study soil C pools of different ages. In this study we evaluated to what extent N enrichment affects soil C dynamics in experiments that applied C isotope tracers. Methods: Using meta-analysis, we synthesized data from 35 published papers. We made a distinction between “new C” and “old C” stocks, i.e., soil C derived from plant C input since the start of the isotopic enrichment, or unlabeled, pre-existing soil C. Results: Averaged across studies, N addition increased new soil C stocks (+30.3%), total soil C stocks (+6.1%) and soil C input proxies (+30.7%). Although N addition had no overall, average, effect on old soil C stocks and old soil C respiration, old soil C stocks increased with the amount of N added and respiration of old soil C declined. Nitrogen-induced effects on new soil C and soil C input both decreased with the amount of extraneous N added in control treatments. Conclusion: Although our findings require additional confirmation from long-term field experiments, our analysis provides isotopic evidence that N addition stimulates soil C storage both by increasing soil C input and (at high N rates) by decreasing decomposition of old soil C. Furthermore, we demonstrate that the widely reported saturating response of plant growth to N enrichment also applies to new soil C storage.

54 ENVIRONMENTAL SCIENCES↗

Hybrid Capture for Net-Negative Emissions

The Energy & Environmental Research Center, in partnership with the U.S. Department of Energy and North Dakota ethanol producer Red Trail Energy, LLC (RTE), completed an initial engineering design (IED) for a potential hybrid capture system at the RTE CCS (carbon capture and storage) site. The RTE CCS Project is currently operating a CO2 capture facility, adjacent to the RTE ethanol facility in western North Dakota, and injecting the CO2 more than a mile below RTE property for permanent storage. This novel process would add capturing CO2 produced from natural gas boilers to the existing bioprocessing capture system to achieve a net-negative CO2 emissions industrial process at commercial scale (~310,000 tonnes/yr CO2). Initial findings of the IED showed the hybrid system to be technically viable with a moderate estimated cost of $55/tonne CO2 captured for the hybrid system. A cradle-to-gate life cycle assessment (LCA) was also conducted, showing preliminary net-negative carbon emissions potential anticipated from implementing a hybrid CCS system at a commercial scale. Recommended next steps toward potential implementation include hybrid capture system demonstration testing for detailed engineering and LCA model comparisons with low-carbon fuel incentive programs for financial support.

01 COAL, LIGNITE, AND PEAT↗

NRAP-Open-IAM Multisegmented Wellbore Reduced-Order Model: Improvement and Quality Assurance

The multisegmented wellbore model (MSW) semi-analytically estimates the amount of CO 2 and brine leakage from a leaking legacy well by segmenting it into intervals to simulate site-specific stratigraphic and hydrogeologic properties. The model is a component of the National Risk Assessment Partnership Open-Source Integrated Assessment Model (NRAP-Open-IAM), which was developed to perform risk assessment for geologic CO 2 storage. The new wellbore leakage model, which uses deep learning networks for a caprock segment, was developed to enhance the analytical MSW. The model was trained and validated using a synthetic data set of Subsurface Transport Over Multiple Phases (STOMP) multiphase flow simulations from various geological, well attribute, and operational conditions to ensure its quality. The results demonstrate that the model is more accurate than the existing model in predicting the transport of two-phase fluids (brine and injected CO 2 ) through the well. This report provides a detailed explanation of the model development and quality assurance.

58 GEOSCIENCES↗

Ion Exchange Processes for CO 2 Mineralization Using Industrial Waste Streams: Pilot Plant Demonstration and Life Cycle Assessment

Abstract An attractive technique for removing CO 2 from the environment is sequestration within stable carbonate solids (e. g., calcite). However, continuous addition of alkalinity is required to achieve favorable conditions for carbonate precipitation (pH>8) from aqueous streams containing dissolved CO 2 (pH<4.5) and Ca 2+ ions. In this study, a pH‐swing process using ion exchange was demonstrated to process 300 L of produced water brine per day for CO 2 mineralization. Proton titration capacities were quantified for aqueous streams in equilibrium with gas streams at various concentrations of CO 2 (pCO 2 =0.03–0.20 atm) and at various flow rates (0.5–2.0 L min −1 ). Energy intensities for the process were determined to be between 30 and 65 kWh per tonne of CO 2 sequestered depending on the composition of the brine stream. A life cycle assessment was performed to analyze the net carbon emissions of the technology which indicated a net CO 2 reduction for pCO 2 ≥0.12 atm (−0.06–−0.39 kg CO 2 e per kg precipitated CaCO 3 ) utilizing calcium‐rich brines. The results from this study indicate the ion exchange process can be used as a scalable method to provide alkalinity necessary for the capture and storage of CO 2 in Ca‐rich waste streams.

Chemistry↗

A comprehensive techno-eco-assessment of CO 2 enhanced oil recovery projects using a machine-learning assisted workflow

Carbon dioxide enhanced oil recovery (CO 2 -EOR) projects not only extract residual oil but also sequestrate CO 2 in the depleted reservoirs. Here, this study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO 2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO 2 -EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO 2 (CO 2 -WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO 2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.

03 NATURAL GAS↗

Designing monitoring networks for local earthquakes

Abstract Seismic networks are essential for monitoring local earthquakes in connection to industrial activities, including wastewater injection and CO2 sequestration. Because these networks are typically deployed for short periods of time at specific sites, it is beneficial to develop best practices for efficient and effective installation and monitoring. Such standards are available for regional earthquake and microseismic monitoring, but not readily available for local scale (tens of kilometer scale) monitoring. Once the region of interest has been determined, the key parameters for establishing a network are available monitoring station locations, site noise, and site utility. Networks should be designed based on the project monitoring goals for earthquake magnitude and density. Herein, a network was established at the Patterson and Hartland Oil Fields in western Kansas in association with a US Department of Energy CarbonSAFE carbon capture and sequestration research project. We employed observations from an analog local network, the Wellington Earthquake Monitoring Network in south-central Kansas, to assess site noise and estimate earthquake detection thresholds for the Patterson and Hartland fields. Noise from oil production facilities was evaluated, concluding that oil lease sites are suitable for monitoring small local earthquakes (M1-M3). The network was designed to have a magnitude of completeness of M1 while using station locations on existing field operator leases. Fifteen months of continuous network operation demonstrates reliable and efficient local earthquake monitoring and provides best practices recommendations for similar operations.

58 GEOSCIENCES↗

Widespread Global Peatland Establishment and Persistence over the Last 130,000 Y

Glacial−interglacial variations in CO2 (exp) and methane in polar ice cores have been attributed, in part, to changes in global wetland extent, but the wetland distribution before the Last Glacial Maximum (LGM, 21 ka to 18 ka) remains virtually unknown. We present a study of global peatland extent and carbon (C) stocks through the last glacial cycle (130 ka to present) using a newly compiled database of 1,063 detailed stratigraphic records of peat deposits buried by mineral sediments, as well as a global peatland model. Quantitative agreement between modeling and observations shows extensive peat accumulation before the LGM in northern latitudes (>40°N), particularly during warmer periods including the last interglacial (130 ka to 116 ka, MIS 5e) and the interstadial (57 ka to 29 ka, MIS 3). During cooling periods of glacial advance and permafrost formation, the burial of northern peatlands by glaciers and mineral sediments decreased active peatland extent, thickness, and modeled C stocks by 70 to 90% from warmer times. Tropical peatland extent and C stocks show little temporal variation throughout the study period. While the increased burial of northern peats was correlated with cooling periods, the burial of tropical peat was predominately driven by changes in sea level and regional hydrology. Peat burial by mineral sediments represents a mechanism for long-term terrestrial C storage in the Earth system. These results show that northern peatlands accumulate significant C stocks during warmer times, indicating their potential for C sequestration during the warming Anthropocene.

Treat, Claire C.↗

Technological evolution of large-scale blue hydrogen production toward the U.S. Hydrogen Energy Earthshot

Hydrogen potentially has a crucial role in the U.S. transition to a net-zero emissions economy. Learning from large-scale hydrogen projects will boost technological evolution and innovation toward the U.S. Hydrogen Energy Earthshot. We apply experience curves to estimate the evolving costs of blue hydrogen production and to further examine the economic effect on technological evolution of the Inflation Reduction Act’s tax credits for carbon sequestration and clean hydrogen. Learning-by-doing alone can decrease the production cost of blue hydrogen. Without tax incentives, however, it is hard for blue hydrogen production to reach the cost target of $\$1$/kg H 2 . Here we show that the breakeven cumulative production capacity required for gas-based blue hydrogen to reach the $\$1$/kg H 2 target highly depends on tax credit, natural gas price, inflation rate, and learning rates. We make recommendations for hydrogen hub development and for accelerating technological progress toward the Hydrogen Energy Earthshot.

08 HYDROGEN↗

Risk Assessment and Management Workflow—An Example of the Southwest Regional Partnership

This paper summarizes the risk assessment and management workflow developed and applied to the Southwest Regional Partnership on Carbon Sequestration (SWP) Phase III Demonstration Project. The risk assessment and management workflow consists of six primary tasks, including management planning, identification, qualitative analysis, quantitative analysis, response planning, and monitoring. Within the workflow, the SWP assembled and iteratively updated a risk registry that identifies risks for all major activities of the project. Risk elements were ranked with respect to the potential impact to the project and the likelihood of occurrence. Both qualitative and quantitative risk analyses were performed. To graphically depict the interactions among risk elements and help building risk scenarios, process influence diagrams were used to represent the interactions. The SWP employed quantitative methods of risk analysis including Response Surface Method (RSM), Polynomial Chaos Expansion (PCE), and the National Risk Assessment Partnership (NRAP) toolset. The SWP also developed risk response planning and performed risk control and monitoring to prevent the risks from affecting the project and ensure the effectiveness of risk management. As part of risk control and monitoring, existing and new risks have been tracked and the response plan was subsequently evaluated. Findings and lessons learned from the SWP’s risk assessment and management efforts will provide valuable information for other commercial geological CO2 storage projects.

Lee, Si-Yong↗

Observing and modeling the effects of production infrastructure in electromagnetic surveys

Electromagnetic (EM) methods are among the original techniques for subsurface characterization in exploration geophysics because of their particular sensitivity to the earth electrical conductivity, a physical property of rocks distinct yet complementary to density, magnetization, and strength. However, this unique ability also makes them sensitive to metallic artifacts — infrastructure such as pipes, cables, and other forms of cultural clutter — the EM footprint of which often far exceeds their diminutive stature when compared to that of bulk rock itself. In the hunt for buried treasure or unexploded ordnance, this is an advantage; in the long-term monitoring of mature oil fields after decades of production, it is quite troublesome indeed. In this study we consider the latter through the lens of an evolving energy industry landscape in which the traditional methods of EM characterization for the exploration geophysicist are applied toward emergent problems in well-casing integrity, carbon capture and storage, and overall situational awareness in the oil field. We introduce case studies from these exemplars, showing how signals from metallic artifacts can dominate those from the target itself and impose significant burdens on the requisite simulation complexity. Furthermore, we show how recent advances in numerical methods mitigate the computational explosivity of infrastructure modeling, providing feasible and real-time analysis tools for the desktop geophysicist. Lastly, we demonstrate through comparison of field data and simulation results that incorporation of infrastructure into the analysis of such geophysical data is, in a growing number of cases, a requisite but now manageable step.

58 GEOSCIENCES↗

Distinct Assembly Processes and Microbial Communities Constrain Soil Organic Carbon Formation

Soil stores more carbon (C) than all vegetation and the atmosphere combined. Soil C stocks are broadly shaped by temperature, moisture, soil physical characteristics, vegetation, and microbial-mediated metabolic processes. The efficiency with which microorganisms use soil C regulates the balance between C storage in soil and the atmosphere. In this review, we discuss how microbial physiology and community assembly processes determine microbial growth rate and efficiency and, in turn, soil organic C cycling through the lens of community ecology. We introduce a conceptual framework cataloging life history (i.e., growth rate, resource acquisition, and stress tolerance) and assembly traits (i.e., competition, facilitation, and dispersal) that correspond with different growth efficiencies. We also compare how dominant mycorrhizal fungal type affects growth efficiency. We propose that further development and inclusion of specific community parameters in microbial-explicit Earth system models are needed for accurately predicting soil organic C responses to global change.

carbon sequestration↗

Illinois Basin Regional DAC Hub TA-1: Feasibility (Phase-0) (Final Scientific/Technical Report)

The overall objective of this project was to complete a feasibility study for a Regional Direct Air Capture (DAC) Hub that encompasses Illinois, Indiana, and Kentucky. The geographic construct of this hub was based on the Illinois Basin – a geological area where at least seven (7) geological storage studies were conducted. The Hub was designed to assure a capacity to capture, store, and utilize at least 1,000,000 metric tons (tonnes) of CO2 from the atmosphere annually 1 metric tonnes per annum (MTA), starting from an initial capacity of 200,000 tonnes of CO2 per annum (KTA). The Hub also included:(1) clean energy providers to supply renewable energy to the DAC units; (2) geological storage facilities to inject and sequestrate the captured CO2; (3) CO2 utilization facilities that would uptake the captured CO2 and convert it into products; and (4) small business organizations that would assist in workforce development efforts for the region. This closeout report consolidates the tasks undertaken and accomplished as deliverables for the project. The project team had successfully completed all required tasks of phase 0a, confirming that the project is feasible based on the preliminary conceptual designs and operational requirements of the DAC systems. Of particular interest, the hub ownership structure was identified and was implemented in follow on projects that would transition the hub infrastructure to a build/operate phase.

36 MATERIALS SCIENCE↗

Pressurized in situ X-ray diffraction insights into super/subcritical carbonation reaction pathways of steelmaking slags and constituent silicate minerals

This study explores mineral carbonation of industrial stainless steelmaking slags and relevant synthetic constituent minerals via in situ pressurized X-ray diffraction, to clarify carbonation reaction pathways and efficiency for carbon storage and waste valorization. The primary mineral phases of Argon Oxygen Decarburization (AOD) and Continuous Casting (CC) slags, namely, åkermanite, bredigite, cuspidine, merwinite, and β- and γ-C 2 S, were reacted in a custom-built beryllium-capped XRD reactor filled with either water-saturated (wet) subcritical CO 2 (g), or wet supercritical CO 2(SC) , in a series of carbonation experiments. Formation of calcite and aragonite was observed for most Ca-bearing minerals, transient precipitation of metastable vaterite was observed, while carbonation of AOD and CC slags in CO 2(SC) resulted in hydrated crystalline calcium carbonates and indirect evidence of amorphous carbonate. Overall, results obtained suggest that low-pressure subcritical carbonation routes are attractive for standalone slag carbonation processes, while high-pressure supercritical carbonation routes are amiable to symbiotic integration with CO 2(SC) -generating green technologies.

42 ENGINEERING↗

Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase) (Final Report)

The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership (RCSP) Initiative. The public/private consortium, funded through the MRCSP, brings together nearly 40 industry partners in 10 contiguous states in Mid-West and North-East regions. Battelle, as the project lead, oversees research & development, operations, and coordinates activities among various partners. Since its founding, MRCSP has made significant progress towards advancing CCS and CCUS technologies for participating states in the region. Between 2008 to 2020, Phase III of the MRCSP program focused on the development of large-scale injection tests. This report is part of a series of topical reports prepared under the MRCSP Phase III. The MRCSP Final Technical Report summarizes the research conducted while monitoring large-scale injection tests at a CO 2 -EOR facility in Northern Michigan. The project successfully injected more than one million metric tons of CO 2 into depleted oil fields in the Northern Niagaran Pinnacle Reef Trend (NNPRT) for enhanced oil recovery (EOR). These commercial-scale tests provided valuable real-world knowledge that has been used to refine technologies and methods, reduce uncertainties, and demonstrate safety and effectiveness of CCUS technologies to increase public acceptance. The Final Technical Report provides an overview of Phase III efforts including advancing the understanding of the regional geology, storage capacities, deployment of many monitoring and modeling techniques and its efficacy, and technical demonstration of large-scale CO 2 injection, containment, and associated storage in the NNPRT region. Specific topical reports on the subjects covered in the Final Technical Report are also available through EDX/OSTI.

01 COAL, LIGNITE, AND PEAT↗