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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multi-Split Variable Refrigerant Flow (VRF) System Building Energy Simulations Using Performance Maps

Multi-split variable refrigerant flow (VRF) systems are highly energy-efficient HVAC (heating, ventilation and air conditioning) technologies that connect a single outdoor unit to multiple independent indoor terminal units using a common refrigerant circuit and a variable-speed compressor. Building energy simulations that incorporate VRF systems help model their unique operational characteristics and predict energy consumption in specific building designs. Traditionally, EnergyPlus models these systems by employing multiple sets of performance curves to characterize both individual terminal units and the outdoor unit. However, producing these curves is labor intensive and error prone, and they often do not capture all the key input and output variables. This paper introduces a novel approach that uses multi-dimensional performance maps to model VRF systems in building environments for space cooling. In this approach, performance maps are developed at the component level—separately for the outdoor unit and for each indoor terminal. The new modeling method is validated within EnergyPlus via a Python plug-in that contains a simple solver loop to coordinate the component-level, indoor, and outdoor unit maps. Furthermore, because performance maps can span more variables than traditional performance curves, they offer the opportunity to implement advanced controls, such as enhanced dehumidification and compressor modulation. A VRF air conditioner’s hardware system was modeled using the DOE/ORNL Heat Pump Design Model, which was automated to produce extensive performance maps for both the indoor and outdoor units.

Shen, Bo [ORNL] (ORCID:0000000336600393)↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2025

Approaching hydro-equivalent ignition in laser direct-drive via target design optimization using novel statistical modeling

Laser direct-drive offers significant advantages in terms of target simplicity, improved energy coupling, and large fuel masses over indirect drive. However, performance degradations from hydrodynamic and laser-plasma instabilities seeded and driven by the direct illumination pose limitations on the parameter space available for achieving ignition. In this paper, new design improvements are identified to forge a path forward for a hydro-equivalent ignition demonstration. The first is related to a new formulation of the statistical model (SM) used to accurately predict target performance directly from input parameters such as laser pulse shape and target specifications. This new SM formulation provides direct guidance on target dimensions and laser beam-to-target radius to achieve the highest fusion yield on the OMEGA laser. The second improvement comes from cooling the deuterium–tritium (DT) ice layer below the triple point right before shot time leading to lower DT vapor densities and higher convergence. Guided by these design improvements, a Bayesian optimization algorithm was used to design an implosion that is predicted to closely approach a Lawson triple product that hydrodynamically scales to ignition if equivalent laser–target coupling is achieved at laser energies typical of the National Ignition Facility.

Deuterium↗
DOE OSTI2025

H2A-Lite (Hydrogen Analysis Lite Production Model) [SWR-24-69]

Within H2A-Lite, users can provide a minimal number of inputs—such as hydrogen production technology of choice —to produce estimates about characteristic scale, capital, and operations. Price projections for energy and feedstock are based on the Energy Information Administration's Annual Energy Outlook 2022, AEO2022 Reference case. The model additionally allows users to override technology default values to adapt to specific technology scales or regional energy prices. As output, H2A-Lite provides cost breakdown from rigorous financial analysis as well as greenhouse gas and criteria pollutant emissions characteristics.

Penev, Michael [National Laboratory of the Rockies↗
DOE OSTI2022

Energy and exergy analysis of multi-stage vacuum membrane distillation integrated with mechanical vapor compression

Membrane distillation (MD) is a promising candidate for desalinating hypersaline brine, but its poor energy efficiency has remained a major barrier for widespread application. One possible solution to this issue is to recover the latent heat in the process. In this work, a multi-stage vacuum MD (MSVMD) was integrated with a mechanical vapor compressor (MVC) to enhance the latent heat recovery, and the energetic and exergetic performance of this integrated process was examined. A comprehensive energy and exergy analysis is provided to compare MSVMD and MSVMD-MVC processes for desalination of hypersaline brine. This analysis was conducted by examining the effect of the compression ratio on the energetic and exergetic performance, and the findings are reported in terms of specific thermal energy consumption (STEC), specific electricity consumption (SEC), and exergetic efficiency. The energy analysis shows that thermal energy consumption can be reduced as the compression ratio increases, due to the enhancement of latent heat recovery. The MSVMD-MVC process can be operated in a steady-state condition, without the need for thermal heat input; with STEC and SEC of 0 and 49 kWh/m 3 at the feed temperature of 50 °C and MVC compression ratio of 2.14. Moreover, exergy analysis demonstrates the efficacy of the eNRTL model in exergy calculation. Exergy destruction can be greatly reduced by increasing the compression ratio to an optimal value. For high salinity brine (124 g/L), MSVMD-MVC achieved a higher exergetic efficiency of 6.85%, compared to 2.42% in MSVMD. Furthermore, the result suggests that the application of MVC can intensify the energy efficiency and exergetic efficiency of the MSVMD system, although this process cannot outperform the current desalination technologies from the standard primary energy point of view.

42 ENGINEERING↗
DOE OSTI2023

Electrical Infrastructure Cost Model for Marine Energy Systems

The National Renewable Energy Laboratory's Electrical Infrastructure Cost Model is an Excel-based tool designed to estimate the electrical infrastructure costs of marine energy components and subsystems. It incorporates data collected from offshore wind projects, utility projects, and other relevant sources to provide accurate and comprehensive cost projections. With its user-friendly interface, the model allows users to input various parameters related to the system array, electrical cables, and substations. By leveraging industry data, cost trends, and technological advancements, the model generates outputs that include system array sizing, electrical cable specifications and costs, substation specifications and costs, and total electrical infrastructure costs. One of the notable strengths of the model is its flexibility in covering multiple-orders-of-magnitude scaled systems, accommodating projects ranging from proof-of-concept or pilot-scale installations to large-scale offshore systems. By collecting data largely from offshore wind reports and utility projects, the model incorporates real-world conditions and accounts for industry-specific factors. It incorporates cost trends and sizing relationships to deliver cost estimations for electrical infrastructure components, such as electrical cables and substation equipment.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Vehicle-Cycle Inventory for Type C School Buses & Intra-City Transit Buses

This report documents the new inventory incorporated into the Research and Development version of Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) 2025 model for the vehicle cycle of Type C school buses and intracity transit buses. The transportation sector contributes significantly to the United States’ energy consumption and resultant emissions (EPA, 2025a). However, public transit plays an important role in mitigating these impacts because it consumes a relatively low amount of energy per passenger (Congressional Budget Office, 2022). Public transit is widely used in the United States; more than 500,000 school buses (EPA, 2025b) and ~75,000 service buses (American Public Transportation Association, 2025) operate in the nation. These are primarily internal combustion engine vehicles (ICEVs) powered by diesel. Original equipment manufacturers (OEMs) are making efforts to electrify U.S. bus fleets by using batteries as a propulsion system to replace internal combustion engines. Electrification can reduce tailpipe emissions, such as particulate matter (with a diameter ≤10 µm [PM 10 ] and with a diameter ≤2.5 µm [PM 2.5 ]) and nitrogen oxides (Jonas et al., 2025; Martinez and Samaras, 2024; EPA, 2025b; Wayne et al., 2009). Hence, any energy and emission impact analysis of public transit must consider both conventional ICEVs and upcoming electric vehicle (EV) options for the school and transit buses that dominate this landscape. To understand the detailed environmental impact profiles of ICEV and EV school and transit buses, it is necessary to conduct a thorough analysis covering both vehicle manufacturing and vehicle use stages. The current literature lacks a detailed vehicle-cycle inventory for school and transit buses, which makes this kind of comparison difficult. To overcome this gap, we developed a comprehensive vehicle-cycle model for school and transit buses in Argonne’s R&D GREET 2025 model. The model is flexible in handling user inputs for key assumptions, such as component weights and material compositions, upstream energy sources for material processing, and vehicle operating parameters, to understand their impacts on energy use and emissions for both school and transit buses. This report is organized as follows: Section 2 provides details on the modeling approach and vehicle specifications (weights and composition of different vehicle components, and vehicle operating parameters) for both school and transit buses. Section 3 provides details on vehicle assembly, disposal, and recycling (ADR) approaches for the two buses. Section 4 includes details about their incorporation into the R&D GREET model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The Science, Engineering, and Validation of Marine Carbon Dioxide Removal and Storage

Scenarios to stabilize global climate and meet international climate agreements require rapid reductions in human carbon dioxide (CO 2 ) emissions, often augmented by substantial carbon dioxide removal (CDR) from the atmosphere. While some ocean-based removal techniques show potential promise as part of a broader CDR and decarbonization portfolio, no marine approach is ready yet for deployment at scale because of gaps in both scientific and engineering knowledge. Marine CDR spans a wide range of biotic and abiotic methods, with both common and technique-specific limitations. Further targeted research is needed on CDR efficacy, permanence, and additionality as well as on robust validation methods—measurement, monitoring, reporting, and verification—that are essential to demonstrate the safe removal and long-term storage of CO 2 . Engineering studies are needed on constraints including scalability, costs, resource inputs, energy demands, and technical readiness. Research on possible co-benefits, ocean acidification effects, environmental and social impacts, and governance is also required.

climate mitigation↗
DOE OSTI2021

NEAMS Workbench MOOSE Integration Update

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench is a graphical user interface (GUI) that provides a common analysis environment for the accelerated use of the NEAMS toolkit. To improve design and analysis of current and future nuclear energy systems, the NEAMS Workbench provides an integrated development environment for model creation, review, execution, output review, and visualization for integrated tools. In addition to a GUI, the NEAMS Workbench provides the Workbench Analysis Sequence Processor, an open-source tool set that facilitates input-content assistance for supported domain-specific input languages and user-friendly syntaxes. These supported syntaxes enable accelerated development and deployment of enhanced user inputs and workflows. The Multiphysics Object-Oriented Simulation Environment (MOOSE) has been supported in the NEAMS Workbench since 2016 and receives continuous updates. Efforts to improve MOOSE-based tool integration in the NEAMS Workbench are ongoing. This document details the current and planned enhancements of the NEAMS Workbench and the MOOSE framework to improve integration and usability of MOOSE-based applications within the NEAMS Workbench.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The Design, Fabrication, and Test Program for NREL's Wave-Powered Desalination System: Preprint

Starting in 2018, the U.S. Department of Energy's Water Power Technologies Office (WPTO), initiated the development of a prize competition as a foundational investment of Powering the Blue Economy, The prize encouraged the development of small, modular, cost-competitive wave-powered desalination systems. The National Renewable Energy Laboratory (NREL) was tasked with managing the prize, known as the Waves to Water Prize (W2W), and providing technical input based on prior desalination research performed at the lab. NREL partnered with the Coastal Studies Institute (CSI) and Jennette's Pier in North Carolina for their expertise in deploying research articles at the Jennette's Pier research facility. The prize consisted of five stages that included high-level concept proposals, numerical modelling, site- specific design, subsystem prototyping, and a final ocean demonstration. Due to the logistical risks of installing numerous prototypes in the ocean at the same time, NREL was tasked with designing and building a test article to de- risk the final event. The test article design needed to represent the technologies expected in the final stage of the prize. This meant that the design was expected to follow the same rules as the competitors, providing CSI with an opportunity to practice installations and develop a final logistics plan prior to the final event. After concluding the W2W event in April 2022, the NREL test article was redeployed in August 2022 to better understand the challenges of anchoring wave energy converters (WECs) in shallow water conditions with breaking waves. For the Spanish version of this report, see NREL/CP-5700-88482 (https://www.nrel.gov/docs/fy24osti/88482.pdf).

deployment↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2024

The Design, Fabrication, and Test Program for NREL's Wave-Powered Desalination System

Starting in 2018, the U.S. Department of Energy's Water Power Technologies Office (WPTO), initiated the development of a prize competition as a foundational investment of Powering the Blue Economy, the prize encouraged the development of small, modular, cost-competitive wave-powered desalination systems. The National Renewable Energy Laboratory (NREL) was tasked with managing the prize, known as the Waves to Water Prize (W2W), and providing technical input based on prior desalination research performed at the lab. NREL partnered with the Coastal Studies Institute (CSI) and Jennette's Pier in North Carolina for their expertise in deploying research articles at the Jennette's Pier research facility. The prize consisted of five stages that included high-level concept proposals, numerical modelling, site-specific design, subsystem prototyping, and a final ocean demonstration. Due to the logistical risks of installing numerous prototypes in the ocean at the same time, NREL was tasked with designing and building a test article to de-risk the final event. The test article design needed to represent the technologies expected in the final stage of the prize. This meant that the design was expected to follow the same rules as the competitors, providing CSI with an opportunity to practice installations and develop a final logistics plan prior to the final event. After concluding the W2W event in April 2022, the NREL test article was redeployed in August 2022 to better understand the challenges of anchoring wave energy converters (WECs) in shallow water conditions with breaking waves.

desalination↗
DOE OSTI2026

Integrating Maximum Entropy Production Theory and Machine Learning to Improve Global Evapotranspiration Modeling

Accurate estimation of terrestrial evapotranspiration (ET) is vital for understanding global water and energy cycles. However, current global ET estimations are not well constrained. This study introduces an integrated framework combining the Maximum Entropy Production (MEP) theory with Random Forest (RF) model to improve global ET estimation. Specifically, in contrast to direct ET estimation by the RF model, the integrated framework (MEP‐RF) trains to predict error of MEP‐simulated ET. MEP‐RF outperforms RF in spatiotemporal extrapolation. Attribution analysis with in situ observations reveals that the inputs of MEP are the most critical variables for the ET process, including net radiation, vegetated area, soil moisture, and surface temperature. We further drive MEP‐RF with global reanalysis and satellite data sets of these four inputs, yielding a global mean terrestrial ET of 548 mm/year, with 77% attributed to transpiration. The global ET increased at a rate of 0.85 mm/year per year during 2003–2021, primarily due to vegetation greening rather than rising temperature, while decreasing soil moisture led to decreasing regional ET. The integrated framework provides a novel approach for the estimation of global ET without the need for hard‐to‐obtain and thus uncertain inputs, such as wind speed, surface roughness, aerodynamic and canopy stomatal resistance. Therefore, MEP‐RF offers an independent method on existing global ET products. It represents a promising physically based approach that can be incorporated into Earth System Models to enhance water and energy cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1

Abstract. Groundwater plays a key role in meeting water demands, supplying over 40 % of irrigation water globally, with this role likely to grow as water demands and surface water variability increase. A better understanding of the future role of groundwater in meeting sectoral demands requires an integrated hydro-economic evaluation of its cost and availability. Yet substantial gaps remain in our knowledge and modeling capabilities related to groundwater availability, recharge, feasible locations for extraction, extractable volumes, and associated extraction costs, which are essential for large-scale analyses of integrated human–water system scenarios, particularly at the global scale. To address these needs, we developed Superwell, a physics-based groundwater extraction and cost accounting model that operates at sub-annual temporal and at the coarsest 0.5° (≈50 km × 50 km) gridded spatial resolution with global coverage. The model produces location-specific groundwater supply–cost curves that provide the levelized cost to access different quantities of available groundwater. The inputs to Superwell include recent high-resolution hydrogeologic datasets of permeability, porosity, aquifer thickness, depth to water table, recharge, and hydrogeological complexity zones. It also accounts for well capital and maintenance costs, as well as the energy costs required to lift water to the surface. The model employs a Theis-based scheme coupled with an amortization-based cost accounting formulation to simulate groundwater extraction and quantify the cost of groundwater pumping. The result is a spatiotemporally flexible, physically realistic, economics-based model that produces groundwater supply–cost curves. We show examples of these supply–cost curves and the insights that can be derived from them across a set of scenarios designed to explore model outcomes. The supply–cost curves produced by the model show that most (90 %) nonrenewable groundwater in storage globally is extractable at costs lower than USD 0.57 m−3, while half of the volume remains extractable at under USD 0.108 m−3. The global unit cost is estimated to range from a minimum of USD 0.004 m−3 to a maximum of USD 3.971 m−3. We also demonstrate and discuss examples of how these cost curves could be used by linking Superwell's outputs with other models to explore coupled human–environmental system challenges, such as water resources planning and management, or broader analyses of multisectoral feedbacks.

Global Change Analysis Model (GCAM)↗
DOE OSTI2026

Transient uncertainty quantification and Global Sensitivity Analysis of the open-source Molten Chloride Reactor Experiment (MCRE) using GP-PCA surrogate models

Uncertainties in the thermophysical properties of molten salts impact both the steady-state and transient behavior of Molten Salt Reactors (MSRs). In this work, we aim to quantify the influence of such uncertainties on the transient operation of the Molten Chloride Reactor Experiment (MCRE), utilizing the open-source specifications provided for this reactor. Seven representative transient scenarios are considered. For each scenario, we evaluate the impact of thermophysical property uncertainties on four key multiphysics model output variables of interest (VoIs): maximum power density, maximum fuel temperature, maximum reflector temperature, and average fuel velocity magnitude. In addition, we perform a Global Sensitivity Analysis (GSA) by computing Sobol’ indices for the uncertain input parameters to determine their contribution to the variability of each VoI. Conducting GSA is computationally intensive due to the large number of required evaluations of the high-fidelity multiphysics model. To mitigate this cost, we develop a surrogate modeling framework that combines Gaussian Process (GP) regression with Principal Component Analysis (PCA), enabling efficient sample generation for the GSA. Our results show that for energy-related VoIs, thermal conductivity is the dominant contributor to uncertainty. In contrast, for flow-related VoIs, density and dynamic viscosity are the primary sources of uncertainty. The specific heat of the fuel salt was found to play a secondary role in the transient analyses.

42 - ENGINEERING↗
DOE OSTI2022

Review of Computer-Aided Manufacturing (CAM) strategies for hybrid directed energy deposition

Hybrid additive manufacturing intertwines both additive and subtractive manufacturing layer by layer to digitally fabricate parts with complex geometries, improved surface finish and tight dimensional accuracies, the sum of which is difficult to obtain with any single process. Computer-Aided Manufacturing (CAM) software is required to orchestrate the machine toolpathing for both the deposition as well as the machining processes and is crucial for the successful fabrication of high quality structures. Additionally, CAM requires substantial operator input to account for challenging aspects of each fabricated structure. For example, deciding at which layer of deposition will the machining process continue to maintain access to complex cavities for finishing - internal features that would otherwise be unfinishable due to reach limitations or obstructions. Moreover, of the many commercially-available hybrid systems, each has a unique kinematic environment which can benefit from specific optimization of toolpath planning and substantial research has directly correlated toolpathing with the microstructure evolution, mechanical properties, porosity and residual stress state of the final fabricated part. This review explores the available strategies for CAM in the context of hybrid direct energy deposition, discusses the advantages and disadvantages of each and considers future CAM trends for this transformational digital manufacturing technology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Shallow Geothermal Resources for Cooling Applications at the University of Hawai'i

Drilling activities account for 30% to 57% of the cost to develop and install a geothermal plant. Therefore, an accurate representation of the cost to drill a well is paramount in techno-economic analysis to determine the feasibility of a geothermal power project. In 2022, the National Renewable Energy Laboratory (NREL) endeavored to revise the U.S. Department of Energy (DOE) GeoVision baseline drilling cost curves due to extensive improvement in drilling rates at the Utah Frontier Observatory Research in Geothermal Energy (FORGE) demonstration site. That effort did not culminate in the recommendation of new curves because the actual project costs did not match the reported performance improvements and were at or above the GeoVision baseline. The need for another iteration of this analysis has arisen from industry record drilling performance reported by recent commercial field-scale and demonstration projects, including Fervo Energy's Cape Station, the Utah FORGE 16B(78)-32 demonstration and the Geysers Power Company's GDC-36 demonstration. Therefore, in this work, we have estimated the resulting industry average rate of penetration (ROP) and bit life and applied these parameters as inputs to the Well Cost Simplified model used in the GeoVision analysis. The resulting revised cost curves show a significant decline from the GeoVision baseline. For vertical wells, the magnitude of cost decline ranges between 12% and 24% while for deviated wells, cost reductions between 18% and 26% are estimated. The revised cost curves are in good agreement with actual cost data and therefore, quantify the economic impact of the utilization of (and advances in) polycrystalline diamond compact (PDC) bit technology and the application of physics-based methodologies that optimize mechanical specific energy.

building cooling↗
DOE OSTI2025

2025 Geothermal Drilling Cost Curves Update: Preprint

Drilling activities account for 30% to 57% of the cost to develop and install a geothermal plant. Therefore, an accurate representation of the cost to drill a well is paramount in techno-economic analysis to determine the feasibility of a geothermal power project. In 2022, the National Renewable Energy Laboratory (NREL) endeavored to revise the U.S. Department of Energy (DOE) GeoVision baseline drilling cost curves due to extensive improvement in drilling rates at the Utah Frontier Observatory Research in Geothermal Energy (FORGE) demonstration site. That effort did not culminate in the recommendation of new curves because the actual project costs did not match the reported performance improvements and were at or above the GeoVision baseline. The need for another iteration of this analysis has arisen from industry record drilling performance reported by recent commercial field-scale and demonstration projects, including Fervo Energy’s Cape Station, the Utah FORGE 16B(78)-32 demonstration and the Geysers Power Company’s GDC-36 demonstration. Therefore, in this work, we have estimated the resulting industry average rate of penetration (ROP) and bit life and applied these parameters as inputs to the Well Cost Simplified model used in the GeoVision analysis. The resulting revised cost curves show a significant decline from the GeoVision baseline. For vertical wells, the magnitude of cost decline ranges between 12% and 24% while for deviated wells, cost reductions between 18% and 26% are estimated. The revised cost curves are in good agreement with actual cost data and therefore, quantify the economic impact of the utilization of (and advances in) polycrystalline diamond compact (PDC) bit technology and the application of physics-based methodologies that optimize mechanical specific energy.

15 GEOTHERMAL ENERGY↗