Critical minerals mining and Native American sovereignty: Comparing case studies of lithium, copper, antimony, nickel and graphite mining in the United States
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Amoah, Macdonald (ORCID:0009000385625455)↗
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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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Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin
Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.
58 GEOSCIENCES↗
Assessing and advancing the potential of quantum computing: A NASA case study
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes.
Rieffel, Eleanor G.↗
Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO 2 and lignin dissolution
The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π*). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R 2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO 2 ) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO 2 into valuable chemicals.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Produced water sharing: Improved economics and reduced community impact – A Pennsylvania case study
Here, hydraulic fracturing for oil and gas extraction from unconventional reservoirs is water intensive. Between water sourced for fracturing purposes and water present in rock formations, operators often produce a greater volume of water than oil or gas. Historically, this surplus of produced water has mainly been disposed of via deep injection wells. Rising disposal costs, seasonally limited water availability, and concerns over induced seismicity have incentivized produced water recycling practices, where an operator uses produced water for hydraulic fracturing operations. The logistical challenges associated with produced water recycling have also encouraged operators to adopt ad-hoc water exchange practices, in which competing operators will exchange produced water for mutual cost savings. In this paper, we investigate the potential benefit from systematic produced water exchange among operators in Northeastern Pennsylvania. We leverage PARETO, a free and open-source modeling framework for produced water management optimization, to quantify the benefits of water exchange practices. In an example drawn from FracFocus data, we find that the adoption of systematic water sharing could improve produced water recycling rates from 49.2% to 99%, decreasing operating and trucking costs.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Static linear density response from X-ray Thomson scattering measurements: a case study of warm dense beryllium
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Physics - Plasma physics↗
Social vulnerability and power loss mitigation: A case study of Puerto Rico
The increasing occurrence of extreme weather events urges us to reevaluate the resiliency and vulnerability aspects of our most critical infrastructures — such as power grids — as their failures result in both economic loss and severe human hardship. Seen through the lens of alleviating human suffering, it is crucial to be able to identify critical system components of the infrastructure for targeted hardening given resource constraints. This effort is of particular importance in islanded areas such as Puerto Rico where hurricanes are frequent and resources are limited, and where the spatially diverse effects of power loss on human suffering are all the more severe. Recent studies on evaluating infrastructure networks during extreme weather events have taken a simulation based approach that incorporates a variety of component models, such as weather realizations, topological network models, fragility models, and power flow models to estimate expected loss of service. Here, in this work, we expand such a Component Based Event Simulation (CBES) methodology proposed in the literature and integrate it with a social vulnerability modeling component. This paradigm-advancing approach of synthesizing the cutting edge capability of power network modeling and the social impacts of the power transmission network failure is demonstrated for the island of Puerto Rico. Our work exemplifies the efficacy of this integrated modeling framework in developing a decision metric for targeted transmission line hardening.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Integrating risk assessment methods for carbon storage: A case study for the quest carbon capture and storage facility
This paper documents how tools developed by the National Risk Assessment Partnership (NRAP) complement the Bowtie risk assessment approach through the demonstration of a risk-based Area of Review (AoR) determination for the Quest Carbon Capture and Storage (CCS) facility near Edmonton, Alberta with the NRAP Open-Source Integrated Assessment Model (NRAP Open-IAM). The Bowtie risk assessment for the Quest CCS site was developed by Shell Canada Ltd., who operates the asset on behalf of the Athabasca Oil Sand Project — a joint venture, owned by Canadian Natural Resources Ltd, Chevron Canada and Shell Canada Ltd. The Quest Bowtie treats site risk comprehensively – considering a large set of technical and project risks (e.g., operational risks, above ground infrastructure, public perception, etc.). NRAP tools, methods, and analyses are focused on quantitative assessment of subsurface technical risks. NRAP-Open-IAM was used to evaluate potential groundwater impacts at the Quest site, i.e., a sufficient change above background groundwater quality levels to be measurable, should CO 2 and/or brine leak from the reservoir via a faulty and/or compromised wellbore. Our results show there is a low potential for groundwater impact and support a further reduction (from ~460 to 100 km 2 ) to the established AoR for the Quest site. Integrating both approaches early in the site characterization period could significantly benefit commercial-scale projects by drastically reducing the region over which leakage risks (e.g., legacy wells) need to be evaluated, i.e., the AoR. This is especially true for brownfield sites, which could contain hundreds to thousands of legacy wells. Further, basing monitoring network design on those regions where impacts are most likely to occur has the potential to facilitate more efficient deployment of monitoring technologies and improve the likelihood of detecting a leak should one occur.
58 GEOSCIENCES↗
Qualitative risk assessment of legacy wells based on publicly available data for class VI well permit applications—Illinois basin case study
Not Available
Arbad, Nachiket (ORCID:0000000215353656)↗
Evaluating offshore legacy wells for geologic carbon storage: A case study from the Galveston and Brazos areas in the Gulf of Mexico
In this article, federal offshore waters in the Gulf of Mexico are of interest for large-scale geologic carbon storage (GCS). However, more than 80,000 offshore oil and gas wells exist in the region, which could impact the integrity of sealing intervals. In this study, we propose a screening methodology for ranking offshore legacy wells based on the challenge they may present to GCS. The methodology relies on the review of well regulatory records to 1) identify leakage pathways and assess the potential hazards that wells pose to planned GCS operations, 2) evaluate well features that impact the accessibility of wells to determine the feasibility of potential corrective actions, and 3) rank wells based on the overall challenge they may pose for GCS. We demonstrate our framework by evaluating the construction and abandonment of 156 wells across eight areas of interest (AOIs) in shallow federal waters along the Texas Gulf Coast. The majority (99.3 %) of wells considered were constructed and plugged in a manner that did not isolate prospective GCS targets in the Upper and Lower Miocene formations and may potentially require a challenging or uncertain corrective action prior to GCS. Dataset trends suggest that the observed well construction and plugging designs may be common in shallow offshore federal waters along the Texas Gulf Coast. Consequently, operators pursuing offshore GCS projects in the region may consider selecting areas that avoid challenging wells or performing robust evaluations of legacy well leakage risks to plan corrective action prior to CO 2 injection.
58 GEOSCIENCES↗
Can section 45Q tax credit foster decarbonization? A case study of geologic carbon storage at Acid Gas Injection wells in the Permian Basin
Carbon capture, utilization, and storage (CCUS) is an important pathway for meeting climate mitigation goals. While the economic viability of CCUS is well understood, previous studies do not evaluate the economic feasibility of carbon capture and storage (CCS) in the Permian Basin specifically regarding the new Section 45Q tax credits. We developed a technoeconomic analysis method, evaluated the economic feasibility of CCS at the acid gas injection (AGI) wells, and assessed the implication of Section 45Q tax credits for CCS at the AGIs. We find that the compressors, well depth, and the permit and monitoring costs drive the facility costs. Compressors are the predominant contributors to capital and operating expenditure driving the levelized cost of CO 2 storage. Strategic cost reduction measures identified include 1) sourcing of low-cost electricity and 2) optimizing operational efficiency in well operations. In evaluating the impact of the tax credits on CCS projects, facility scale proved decisive. We found that facilities with an annual injection rate exceeding 10,000 MT storage capacity demonstrate economic viability contingent upon the procurement of inputs at the least cost. The new construction of AGI wells were found to be economically viable at a storage capacity of 100,000 MT. The basin is heavily focused on CCUS (tax credit – $\$$65/MT CO 2 ), which overshadows CCS ($\$$85/MT CO 2 ) opportunities. Balancing the dual objectives of CCS and CCUS requires planning and coordination for optimal resource and pore space utilization to attain the basin's decarbonization potential. We also found that CCS on AGI is a lower cost CCS option as compared to CCS on other industries.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Capacity Assessment and Cost Analysis of Geologic Storage of Hydrogen: A Case Study in Intermountain-West Region USA
Hydrogen is an integral component of the current energy transition roadmap to decarbonize the economy and create an environmentally-sustainable future. However, surface storage options (e.g., tanks) do not provide the required capacity or durability to deploy a regional or nationwide hydrogen economy. In this study, we have analyzed the techno-economic feasibility of the geologic storage of hydrogen in depleted gas reservoirs, salt caverns, and saline aquifers in the Intermountain-West (I-WEST) region. We have identified the most favorable candidate sites for hydrogen storage and estimated the volumetric storage capacity. Our results show that the geologic storage of hydrogen can provide at least 72% of total energy consumption of the I-WEST region in 2020. We also calculated the capital and levelized costs of each storage option. We found that a depleted gas reservoir is the most cost-effective candidate among the three geologic storage options. Interestingly, the cushion gas type plays a significant role in the storage cost when we consider hydrogen storage in saline aquifers. The levelized costs of hydrogen storage in depleted gas reservoirs, salt caverns, and saline aquifers with large-scale storage capacity are approximately $\$$1.15, $\$$2.50, and $\$$3.27 per kg of H 2 , respectively. Here, this work provides essential guidance for the geologic hydrogen storage in the I-WEST region.
08 HYDROGEN↗
System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study
Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.
Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗