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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Geospatial assessment of the economic opportunity for reforestation in Maryland, USA

Afforestation and reforestation have the potential to provide effective climate mitigation through forest carbon sequestration. Strategic reforestation activities, which account for both carbon sequestration potential (CSP) and economic opportunity, can provide attractive options for policymakers who must manage competing social and environmental goals. In particular, forest carbon pricing can incentivize reforestation on private land, but this may require landholders to forego other profits. Here, we utilize an ambitious geospatial approach to quantify economic opportunities for reforestation in the state of Maryland (USA) based on high-resolution remoting sensing, ecosystem modeling, and economic analysis. Our results identify spatially-explicit areas of economic opportunity where the potential revenue from forest carbon outcompetes the expected profit of existing cropland at the hectare scale. Specifically, we find that under a baseline economic scenario of 20 dollars per ton of carbon (5% rental rate) and decadal average crop profitability, a transition to forest on agricultural land would be more profitable than 23.2% of cropland in Maryland under a 20-year land-use commitment. Accounting for variations in carbon and crop pricing, 5.5% to 55.4% of cropland would be immediately outcompeted by expected forest carbon revenue, with the potential for an additional 0.5% to 10.6% of outcompeted cropland within 20 years. Under the baseline economic scenario, an annual allocation of $5.8 million towards a carbon rental program could protect 6.93 Tg C (2.2% of the state’s total CSP) on reforested croplands. This moderate yearly cost is equal to 9.7% of Maryland’s average annual auction proceeds from participation in the Regional Greenhouse Gas Initiative (between 2014-2018), and 19.3% of the average annual subsidy payments for corn, soy, and wheat allocated over the same period. This methodological approach may be useful for state governments, not-for-profit organizations, or regional climate initiatives interested in identifying strategic areas for reforestation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Westinghouse advanced fuel management strategies leveraging high enrichment and high burnup fuel to optimize PWR economics

In the US, nuclear power plants have been focused on increasing electricity generation for over three decades via longer operating cycles, reduced number of outages and duration, and the implementation of power up-rates. The potential for zero-carbon emission credits and other carbon-reduction initiatives further makes nuclear power utilities to focus on increasing generation capability. Longer operating cycles provide increased energy generation and enable economic savings to nuclear utilities when the increase in fuel loading required to achieve the extended cycle energy target and resulting increase in total fuel cost is offset by the savings from outage avoidance and reduced replacement power costs. For the current licensed limits of 5 w/o {sup 235}U enrichment and 62 GWd/tU peak pin burnup, higher power density PWR plants, which are prevalent in the PWR fleet, require an excessively penalizing fraction of feed fuel assemblies to operate for the extended cycle duration, with inevitably poor fuel use and fuel cycle economics that cannot be counterbalanced by the savings on outage cost avoidance. To overcome this barrier, the nuclear industry is pursuing development of high enrichment/high burnup fuel technology ({sup 235}U enrichment up to 8 w/o and peak pin burnup up to 75 GWd/tU) which increases fuel energy generation capability and can enable high power density plants to achieve positive economics on 24-month cycle of operation. This paper presents advanced fuel management strategies developed by Westinghouse to enable transition of PWR reactors from 18-month to 24-month cycle of operation, with and without a core thermal power uprate, which are then used as basis to assess the economics of a direct transition from 18 to 24-month cycles with high enrichment/high burnup fuel. The results show that these fuel management strategies, especially if coupled with thermal power up-rates, present the opportunity for substantial economic benefits to utilities. The systematic review of fuel management strategies presented can provide an illustrative reference for decision makers as they consider how to maximize nuclear energy generation economically. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Updated Economic Model for Estimation of GDP Losses in the MACCS Offsite Consequence Analysis Code RDEIM Model Report for MACCS v4.2

This report updates the Regional Disruption Economic Impact Model (RDEIM) GDP-based model described in Bixler et al. (2020) used in the MACCS accident consequence analysis code. MACCS is the U.S. Nuclear Regulatory Commission (NRC) used to perform probabilistic health and economic consequence assessments for atmospheric releases of radionuclides. It is also used by international organizations, both reactor owners and regulators. It is intended and most commonly used for hypothetical accidents that could potentially occur in the future rather than to evaluate past accidents or to provide emergency response during an ongoing accident. It is designed to support probabilistic risk and consequence analyses and is used by the NRC, U.S. nuclear licensees, the Department of Energy, and international vendors, licensees, and regulators. The update of the RDEIM model in version 4.2 expresses the national recovery calculation explicitly, rather than implicitly as in the previous version. The calculation of the total national GDP losses remains unchanged. However, anticipated gains from recovery are now allocated across all the GDP loss types – direct, indirect, and induced – whereas in version 4.1, all recovery gains were accounted for in the indirect loss type. To achieve this, we’ve introduced new methodology to streamline and simplify the calculation of all types of losses and recovery. In addition, RDEIM includes other kinds of losses, including tangible wealth. This includes loss of tangible assets (e.g., depreciation) and accident expenditures (e.g., decontamination). This document describes the updated RDEIM economic model and provides examples of loss and recovery calculation, results analysis, and presentation. Changes to the tangible cost calculation and accident expenditures are described in section 2.2. The updates to the RDEIM input-output (I-O) model are not expected to affect the final benchmark results Bixler et al. (2020), as the RDEIM calculation for the total national GDP losses remains unchanged. The reader is referred to the MACCS revision history for other cost modelling changes since version 4.0 that may affect the benchmark. RDEIM has its roots in a code developed by Sandia National Laboratories for the Department of Homeland Security to estimate short-term losses from natural and manmade accidents, called the Regional Economic Accounting analysis tool (REAcct). This model was adapted and modified for MACCS. It is based on I-O theory, which is widely used in economic modeling. It accounts for direct losses to a disrupted region affected by an accident, indirect losses to the national economy due to disruption of the supply chain, and induced losses from reduced spending by displaced workers. RDEIM differs from REAcct in in its treatment and estimation of indirect loss multipliers, elimination of double-counting associated with inter-industry trade in the affected area, and that it is intended to be used for extended periods that can occur from a major nuclear reactor accident, such as the one that occurred at the Fukushima Daiichi site in Japan. Most input-output models do not account for economic adaptation and recovery, and in this regard RDEIM differs from its parent, REAcct, because it allows for a user-definable national recovery period. Implementation of a recovery period was one of several recommendations made by an independent peer review panel to ensure that RDEIM is state-of-practice. For this and several other reasons, RDEIM differs from REAcct.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Technical and Economic Assessment and Gap Analysis of Advanced Nuclear Reactor Integration with a Reference Oil Refinery

Efforts to identify the most-economic methods to decarbonize several sectors of the U.S. economy are underway. Industrial processes such as crude-oil refining rely heavily on energy-dense and easily stored and transported fossil fuels for powering their operations. Refineries use large amounts of energy, primarily derived from fossil sources to separate crude-oil components, break down heavier hydrocarbons into lighter compounds, remove impurities, reform hydrocarbon molecules, and generate steam and electricity for pumps and compressors and other various auxiliary systems. Crude-oil refining operations such as distillation, cracking, desulfurization, reforming, utilities systems and some offsite facilities collectively account for most of the energy consumption. Other operations such as hydrocracking or hydrotreating also require hydrogen for developing hydrogenation reactions which involve substantial heating to keep the reactors at high-temperature and pressure levels. All heat and energy demands are typically provided by natural gas (NG), oil, or other fuels, which makes refinery industry one of the most-difficult sectors to decarbonize. Nuclear power is a viable and energy-dense source of clean electricity, heat, and hydrogen to provide the large, sustainable energy supply that the refining industry demands. The U.S. Department of Energy’s (DOE’s) Integrated Energy Systems (IES) program is working to perform research and development, design, economic siting, and risk analysis. This state-of-the-art work will enable the first on-site demonstrations and commercial deployments of advanced small modular nuclear reactors (SMNRs) integrated with industries such as chemical production, refining, iron and steel making, and more. IES seeks to demonstrate the ability of advanced nuclear reactors to meet the heat and power demands of these industries while reducing carbon emissions in a sustainable and cost-competitive way. The primary objective of this research effort is to analyze industrial-scale SMNR integration intended to decarbonize refining facilities. The foreseen outcome is the provision of reliable, cost-competitive, and sustainable clean energy, alongside a reduction of carbon emissions. Specifically, the focus of this work lies on meeting the reference facilities’ heat and electricity demands with nuclear power while also supplying clean hydrogen via integrated high-temperature steam electrolysis (HTSE). This report presents a comprehensive technical and economic assessment of the integration of advanced nuclear reactors into a reference refinery, leveraging financial incentives from the Inflation Reduction Act (IRA). The evaluation aims to explore the potential economic benefits and challenges associated with incorporating advanced nuclear reactors into refinery operations, particularly in terms of energy efficiency, economic implications and environmental impact. By examining both the technical feasibility and economic viability, this analysis seeks to identify existing gaps and propose solutions for successful nuclear integration implementation. The findings are intended to provide valuable insights for stakeholders considering the adoption of advanced nuclear reactors in the refining sector. A refinery reference-plant was developed, using an open-source refinery model, Petroleum Refinery Lifecycle Inventory Model (PRELIM) and expert assessment, as a base case for comparison with various nuclear integration options. The capacity of 100 kbd/day (KBD) of heavy crude-oil feed was selected to represent a general coking-type refinery with deep conversion capabilities (incorporating heavy-oil upgrading with FCC, coking, and associated hydrotreating process units), using a heavy crude-oil feed, which represents about 70% of U.S. refineries configurations. A summary of all cases considered in this study is shown in Table 1.

13 HYDRO ENERGY↗
DOE OSTI2022

FECM/NETL Unconventional Shale Well Economic Model (UShWEM)

FECM/NETL Unconventional Shale Well Economic Model (UShWEM) is an Excel-based model that evaluates the economics of an unconventional shale well on a per-well and per-pad basis. The model calculates the net cash flow, internal rate of return (IRR), net present value (NPV), earnings before interest, taxes, depreciation, and amortization (EBITDA), payout month and year, and breakeven price (for either oil- or gas-wells). The model can be used to estimate the economics of a well or pad over its lifetime (development through site reclamation) based on (1) the capital and operating costs associated with well/pad development and operations, (2) the revenue associated with oil, gas, and condensate production streams, and (3) accounting for relevant tax policies and asset depreciation applicable for oil and gas operations. The main input for the model is the completion design and production data. Key financial considerations in the model include oil, gas, and condensate market prices, tax-related settings, royalty rates, the discount rate, minimum economic hurdle (IRR) [if performing break-even analysis], and project contingency. The financial consideration can be adjusted to reflect the level of granularity the user requires as input when calculating the economics for a well or pad development. In addition, the model affords users the option to provide their user inputs for all cost categories considered. As a result, the model can be used to generate a multitude of scenario cases for sensitivity analysis of the various financial considerations, as well as production and cost profiles. To make this seamless, the model has the capability for key economic outputs to be exported in large batches through macros-enabled functions on its “Model Output Summary” and “Multi-Well Cost Analysis. The spreadsheet model includes macros and user-defined functions, so the user must enable Excel’s macro capability for the model to function correctly.

Sheriff, Alana↗
DOE OSTI2022

A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. Here, in this paper, we present a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.

25 ENERGY STORAGE↗
DOE OSTI2022

Physical solvents and techno-economic analysis for pre-combustion CO 2 capture: A review

In this manuscript we review the use, development, and economic performance of physical solvents for pre-combustion CO 2 capture from high pressure H 2 rich syngas streams. Commercially available physical solvents are presented, followed by an assessment of the ideal properties that are important for development of novel solvents for CO 2 capture from high-pressure syngas streams. To compare the technical and economic performance of traditional and novel physical solvents, a review of the methods, assumptions and models used in techno-economic analysis (TEA) studies was conducted. It was found that, although some novel solvents show promising technical performance in the laboratory (e.g., high CO 2 absorption capacity and low vapor pressure), other issues (e.g., solvent viscosity and cost) may limit their industrial applications. Process simulations were useful tools for modeling the technical performance of processes using traditional and novel solvents. However, model predictions are most reliable when the methods and correlations used to develop the process simulation are validated with representative experimental data, in particular highly accurate baseline models are required for fair comparison among physical solvents. The key inputs and assumptions in pre-combustion CO 2 capture TEAs have also been summarized. Some studies showed that the promising technical performance of novel physical solvents can be offset by the high and often unknown costs of these solvents. Future development of novel physical solvents for pre-combustion CO 2 capture will benefit from more studies that conduct in-depth techno-economic analysis, specifically with validated process simulations and transparent economic models.

42 ENGINEERING↗
DOE OSTI2023

Uncertainty analysis for techno-economic and life-cycle assessment of wet waste hydrothermal liquefaction with centralized upgrading to produce fuel blendstocks

Wet waste hydrothermal liquefaction is a promising technology for producing transportation fuels with much lower greenhouse gases emissions than petroleum-based fuels. However, its techno-economic and life cycle assessment are primarily based on laboratory scale testing data, subject to considerable uncertainties, and even bias, due to knowledge gaps. Here, a preliminary uncertainty analysis of key economic measures was conducted based on the 2019 state-of-technology model for biocrude production. Building on the preliminary analysis, this work presents a comprehensive uncertainty analysis in both economic and environmental measures of the entire supply chain of wet waste hydrothermal liquefaction to fuel blendstocks including biocrude upgrading based on the 2021 state-of-technology model. The analysis includes the most recent developments in hydrothermal liquefaction and biocrude upgrading technologies and Monte Carlo simulation based on an integrated model system including an improved reactor yield model, reduced-order process model, discounted cash flow economic model and simplified life-cycle assessment model. The estimated biocrude yield ranges from 42.2% to 52.4% with a median of 47.3%. The estimated fuel yield ranges from 34.7% to 42.7% with a median of 38.7%. The estimated minimum fuel selling price ranges from $\$ $2.28/gge to $\$ $3.45/gge with a median of $\$ $2.80/gge. Relative to petroleum-derived diesel, the estimated reduction in supply chain greenhouse gas emissions ranges from 73.4% to 81.8% with a median of 77.7%. Compared to the 2019 state-of-technology analysis, a significant improvement in biocrude selectivity and economic measures and reduction in uncertainties were achieved due to the incorporation of additional continuous experimental data sets, technology development and de-risking, and improvement in model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Economic analysis of precious metal recovery from electronic waste through gas-assisted microflow extraction

The recycling of end-of-life (EoL) electronic products is motivated by the enormous investment of resources in their creation and the environmental concerns associated with electronic waste (e-waste). Hydrometallurgical methods that utilize conventional leaching and solvent extraction are often applied to extract target materials from e-waste; however, these techniques have significant technical and economic limitations when extracting high-value, low concentration metals from complex waste streams. This study proposes and evaluates a novel process based on gas-assisted microflow extraction (GAME) that efficiently recovers precious metals from waste printed circuit boards (WPCBs). Further, an economic analysis is conducted to verify the economic feasibility of the GAME-based process at an industrial scale. The economic outputs are further investigated to identify the most cost-effective production strategies, particularly with respect to the plant feedstock rate. It is envisioned that this study may establish a paradigm for making economically-informed decisions for sustainable technologies.

42 ENGINEERING↗
DOE OSTI2022

Techno-economic and life cycle assessment of aluminum electrorefining from mixed scraps using ionic liquid

Aluminum production from bauxite ore uses significantly high amount of energy and capital expenditure. Recycle and reuse of aluminum can be economical and minimize the environmental impacts. Smelter based recycle and reuse of aluminum is used in recent days, however, it also uses high amount of energy with high cost of production and yields high life cycle impacts. The University of Alabama has developed aluminum electrorefining technology from mixed scraps using ionic liquids as an alternative to traditional smelter based recycle and reuse. This study has explored the techno-economical, and life cycle viability of that technology. An excel-based techno-economic and life cycle assessment model was developed at Idaho National Laboratory for techno-economic and life cycle assessment. SimaPro was used to get the necessary database for the life cycle assessment. This study determined that a 20,000 kg/day ionic liquid-based electrorefining system can be profitable with a net yearly profit of $2.00 million. Further, in terms of net global warming potential, it emits 0.92 kg CO 2 equivalent per kg of aluminum recycled, whereas the traditional smelter-based recycle technology emits 1.57 kg CO 2 equivalent per kg of aluminum recycled, and the aluminum production from bauxite ore emits 17.8 kg CO 2 equivalent per kg of aluminum produced. In other life cycle assessment categories, electrorefining of aluminum emits >88 % less than aluminum production from bauxite ore and it is also better than traditional aluminum recycling in six out of ten categories studied. This makes ionic liquid-based electrorefining technology a very promising technology in terms of process economics and environmental sustainability.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Economic losses from extreme weather in the U.S. Gulf Coast region: spatially differential contributions of climate hazard and socioeconomic exposure and vulnerability

Worldwide economic losses from extreme weather events (EWE) have increased over recent decades, with significant geographic heterogeneity in damages. The IPCC defines the risk from EWE as a function of the climate hazard, socioeconomic exposure, and vulnerability. Although these three drivers vary at fine spatial scales, spatial variability largely has been overlooked in assessments of the drivers of economic loss from EWE. Using cluster analysis, we developed a novel socio-climate hazard typology (SCT) that integrates locally defined climate hazard and socioeconomic exposure and social vulnerability typologies. The results identified 838 unique SCT types impacted by EWE across the Gulf Coastal United States during 1981–2010. We regressed the SCT types and their constituent hazard and socioeconomic components against the cumulative economic loss (1981–2010) from EWE for each SCT type. Across the landscape, economic damages of SCT types were determined by unique, spatially explicit combinations of different risk factors, even in explaining the same level of economic loss. For example, multi-billion-dollar damages in the central Gulf Coast and peninsular Florida were explained by different drivers of risk, with damages in the former explained by additive interaction between climate hazard and multiplicative interaction between climate hazard and socioeconomic exposure and vulnerability, and in the latter explained by socioeconomic exposure and vulnerability. These results highlight the need to diagnose additive and multiplicative interactions among drivers of EWE risk in a spatially explicit context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Can socio-economic indicators of vulnerability help predict spatial variations in the duration and severity of power outages due to tropical cyclones?

Abstract Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

The Economic Impact of Battery Degradation Modelling Uncertainty

Battery energy storage systems (BESS) are used for a variety of applications, with their economic benefit often being the decisive factor for deployment. A multitude of physico-chemical aging mechanisms lead to capacity fade over a BESS life cycle. The models that are used to describe this capacity fade are prone to inherent model errors. Through a holistic techno-economic modelling approach, we investigate the impact of battery degradation modelling uncertainty on the economic benefit of representative BESS applications. Here, it is shown how improved parameter fit quality can reduce the resulting economic uncertainty. Furthermore, we highlight that the consideration of degradation modelling uncertainty is especially crucial when: (i) the cash flow highly depends on the available battery capacity, (ii) a fixed, e.g. warranty mandated, state of health limit acts as the threshold for battery end-of-life, (iii) long evaluation periods and low discount rates are the focus of economic evaluation.

aging↗
DOE OSTI2020

A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in this stochastic economic dispatch problem for forecasting purposes, the conventional Monte-Carlo method is prohibitively time-consuming for practical applications. To overcome this problem, here we propose a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling as the computer experimental design, a Gaussian-process emulator is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated stochastic economic dispatch model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

17 WIND ENERGY↗
DOE OSTI2022

Techno-economic assessment for the production of algal fuels and value-added products: opportunities for high-protein microalgae conversion

Abstract Background Microalgae possess numerous advantages for use as a feedstock in producing renewable fuels and products, with techno-economic analysis (TEA) frequently used to highlight the economic potential and technical challenges of utilizing this biomass in a biorefinery context. However, many historical TEA studies have focused on the conversion of biomass with elevated levels of carbohydrates and lipids and lower levels of protein, incurring substantial burdens on the ability to achieve high cultivation productivity rates relative to nutrient-replete, high-protein biomass. Given a strong dependence of algal biomass production costs on cultivation productivity, further TEA assessment is needed to understand the economic potential for utilizing potentially lower-cost but lower-quality, high-protein microalgae for biorefinery conversion. Results In this work, we conduct rigorous TEA modeling to assess the economic viability of two conceptual technology pathways for processing proteinaceous algae into a suite of fuels and products. One approach, termed mild oxidative treatment and upgrading (MOTU), makes use of a series of thermo-catalytic operations to upgrade solubilized proteins and carbohydrates to hydrocarbon fuels, while another alternative focuses on the biological conversion of those substrates to oxygenated fuels in the form of mixed alcohols (MA). Both pathways rely on the production of polyurethanes from unsaturated fatty acids and valorization of unconverted solids for use as a material for synthesizing bioplastics. The assessment found similar, albeit slightly higher fuel yields and lower costs for the MA pathway, translating to a residual solids selling price of $899/ton for MA versus $1033/ton for MOTU as would be required to support a $2.50/gallon gasoline equivalent (GGE) fuel selling price. A variation of the MA pathway including subsequent upgrading of the mixed alcohols to hydrocarbon fuels (MAU) reflected a required solids selling price of $975/ton. Conclusion The slight advantages observed for the MA pathway are partially attributed to a boundary that stops at oxygenated fuels versus fungible drop-in hydrocarbon fuels through a more complex MOTU configuration, with more comparable results obtained for the MAU scenario. In either case, it was shown that an integrated algal biorefinery can be economical through optimal strategies to utilize and valorize all fractions of the biomass.

09 BIOMASS FUELS↗
DOE OSTI2022

Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.

The Bello et al. study evaluates the impact of contrasting pressure drawdown on gas productivity and the resulting economics of a well in the Marcellus Shale of the Appalachian Basin. This research applies a techno-economic analysis approach to help identify potential ways pressure management strategies can be used to improve cumulative recovery of hydraulically fractured horizontal wells while maintaining project profitability. Gas production forecast outlook scenarios of the Marcellus Shale Energy and Environment Laboratory Laboratory's MIP-3H well were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning (PIML) workflow and 2) via traditional reservoir simulation in Computer Modeling Group’s (CMG) GEM Compositional & Unconventional Simulator. Cash flow and other economic metrics of interest were compiled on the production outlook using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Unconventional Shale Well Economic Model (UShWEM).The sheets within this Microsoft ExcelTM workbook provide the economic metric outputs for the baseline condition and the one-at-a-time (OAT) sensitivity analysis of UShWEM's input parameters for each of the production scenarios evaluated.

Fracture Network Model↗
DOE OSTI2025

NEWTS Economic Data Dashboard: Critical Materials for Energy

The NEWTS Economic Data Dashboard: Critical Materials for Energy is an economic screening tool for assessing the concentration and potential value of the 18 critical materials for energy in fossil energy-related wastewater across the United States. Datasets used to develop the dashboard and complete economic calculations are available as supplementary downloads. These resources were developed primarily using geochemical composition and volume data from the NEWTS Integrated dataset (version 1.0). Energy-related wastewater types presented in the dashboard include produced water (PW), brackish groundwater (BW), acid mine drainage (AMD), coal combustion residual leachate (CCRL), power plant flue gas desulfurization wastewater (FGD), and geothermal fluids. The concentrations of the following critical minerals were included in the analysis, when available: Al, Co, Cu, Dy, F, Ga, Ge, C, Ir, Li, Mg, Mn, Nd, Ni, Pt, Pr, Si, Tb. This economic screening tool was built to support identification of promising critical mineral feedstocks and economic research targets.

Critical Minerals; Critical Minerals and Materials↗
DOE OSTI2026

Reservoir Characterization and Techno-Economic Analysis of Enhanced Geothermal and Closed-Loop Systems in the Wattenberg Field of the Denver-Julesburg Basin, Colorado

This report provides details of reservoir characterization and techno-economic analysis for enhanced geothermal systems and closed-loop geothermal systems in the Denver-Julesburg Basin Wattenberg area. The analysis contributes to the Geothermal Limitless Approach to Drilling Efficiencies (GLADE) project, a multi-institutional research initiative focused on advancing geothermal energy development by reducing drilling costs and improving the rate of penetration. The GLADE project brings together national laboratories, academic institutions, and industry partners, including the U.S. Department of Energy's Geothermal Technologies Office, Occidental Petroleum, the National Laboratory of the Rockies (NLR), Los Alamos National Laboratory, Colorado School of Mines, Louisiana State University, Texas A&M University, and several drilling technology companies. As part of this effort, NLR developed and applied slender-body theory modeling tools and the GEOPHIRES simulator to support techno-economic analysis. The slender-body theory wellbore simulator evaluated thermal performance in U-loop, Eavor-type multilateral, and enhanced geothermal system well configurations. Results indicated that an optimized single U-loop design delivers an outlet temperature of 168.8 degrees Celsius,stabilizing at a final outlet temperature of 135.3 degrees Celsius. Heat production also improved significantly, with an average thermal output of 16.9 MWth and a final thermal output of 11.0 MWth. These results provided critical baseline data for selecting optimized systems for techno-economic analysis. Building on these outputs, NLR applied the GEOPHIRES techno-economic simulator to estimate the levelized cost of electricity for both the Eavor multilateral and enhanced geothermal systems. The sensitivity analysis showed that drilling costs exert a particularly strong impact on the levelized cost of electricity of multilateral systems. Therefore, advances in drilling technology and improved drilling rates could yield significant gains in the economic performance of these systems. In this context, the GLADE project directly supports the U.S. Department of Energy's mission to reduce the cost of geothermal energy and accelerate the deployment of next-generation geothermal technologies.

15 GEOTHERMAL ENERGY↗