Results for “Performance Rating Method” — Page 34

DOE OSTI2019

Working fluid selection for organic Rankine cycle power generation using hot produced supercritical CO 2 from a geothermal reservoir

Geothermal heat mining simulations using supercritical CO 2 (sCO 2 ) were performed in this research. Working fluid selection criteria for power generation using sCO 2 from a geothermal reservoir are then presented for subcritical, superheated and supercritical organic Rankine cycles (ORCs). Meanwhile, method of working fluid classification for ORC is proposed. To get the most feasible ORC design, this study introduces the concept of “turning point” for isentropic and dry working fluids, as well as minimum turbine inlet temperature for wet working fluids. A thermodynamic model was developed with capabilities to obtain the optimal working fluid mass flow rate, evaporation temperature, superheated temperature, and supercritical pressure, to evaluate the thermal performance of the three ORC approaches using hot produced sCO 2 . With this model, thirty potential working fluids with critical temperatures in the range from 50 to 225 °C were screened for utilizing hot produced sCO 2 considering physical properties, environmental and safety impacts, and thermodynamic performances. Finally, the thermodynamic results were compared for all possible working fluids.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Model-Based Extracted Water Desalination System for Carbon Sequestration

Over the last 1.5 years, GE Global Research and Pennsylvania State University defined a model-based, scalable, and multi-stage extracted water desalination system that yields clean water, concentrated brine, and, optionally, salt. The team explored saline brines that ranged across the expected range for extracted water for carbon sequestration reservoirs (40,000 up to 220,000 ppm total dissolved solids, TDS). In addition, the validated the system performance at pilot scale with field-sourced water using GE’s pre-pilot and lab facilities. This project encompassed four principal tasks, in addition to Project Management and Planning: 1) identify a deep saline formation carbon sequestration site and a partner that are suitable for supplying extracted water; 2) conduct a techno-economic assessment and down-selection of pre-treatment and desalination technologies to identify a cost-effective system for extracted water recovery; 3) validate the downselected processes at the lab/pre-pilot scale; and 4) define the scope of the pilot desalination project. Highlights from each task are described below: Deep saline formation characterization The deep saline formations associated with the five DOE NETL 1260 Phase 1 projects were characterized with respect to their mineralogy and formation water composition. Sources of high TDS feed water other than extracted water were explored for high TDS desalination applications, including unconventional oil and gas and seawater reverse osmosis concentrate. Technoeconomic analysis of desalination technologies Techno-economic evaluations of alternate brine concentration technologies, including humidification-dehumidification (HDH), membrane distillation (MD), forward osmosis (FO), turboexpander-freeze, solvent extraction and high pressure reverse osmosis (HPRO), were conducted. These technologies were evaluated against conventional falling film-mechanical vapor recompression (FF-MVR) as a baseline desalination process. Furthermore, a quality function deployment (QFD) method was used to compare alternate high TDS desalination technologies to FF-MVR. High pressure reverse osmosis was found to a be a promising alternative desalination technology. A deep-dive technoeconomic analysis of HPRO was performed, including Capex and Opex estimates, for seawater RO (SWRO). Additionally, two additional cases were explored: 1) a comparison of a SWRO plus HPRO system to the option of doubling the size of a standard seawater RO system to achieve the same total pure water recovery rate; and 2) a flue gas desulfurization wastewater treatment zero-liquid discharge (ZLD) application, where preconcentration with RO (SWRO or SWRO + HPRO) before evaporation and crystallization was compared to FF-MVR and crystallization technologies without RO preconcentration. Pre-pilot process validation Pre-pilot-scale tests were conducted using field production water to validate key process steps for extracted water pretreatment. Approximately 5,000 gallons of field produced water was processed through, microfiltration, ultrafiltration, and steam regenerable sorbent operations. Smaller quantities were processed through microclarification. In addition, analytical methods (purge-and-trap gas chromatography and Hach TOC analytical methods) were validated. Lab-scale HPRO elements were constructed and tested at high pressures, to identify and mitigate technical risks of the technology. Lastly, improvements in RO membrane materials were identified as the necessary next step to achieve further improvement in element performance at high pressure. Scope of Field Pilot A field pilot for extracted water pretreatment was designed.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Accelerating transients with NekRS: GPU overlapping domain implementation and multi-rate timestepping

The simulation of nuclear transients using Computational Fluid Dynamics (CFD) presents significant computational challenges due to the inherent complexity and the wide separation in temporal scales between various flow physical phenomena. These disparities lead to high computational costs, often making the simulation of transients impractical without advanced techniques. Consequently, multiple research initiatives are being pursued by the NEAMS thermal-hydraulic area, some driven by academic institutions and some by national laboratories. Overall, they are exploring novel methods to make transient simulations more feasible and efficient. This report delves into recent advancements within the CFD code NekRS, specifically those achieved in Fiscal Year 2024 under the CONNECT effort, aimed at improving the performance and feasibility of transient simulations. The first major advancement involves the porting of NekRS to Aurora, one of the Department of Energy’s (DOE) most powerful supercomputers. Additionally, the report discusses the implementation of an overlapping domain capability within NekRS. This novel GPU-accelerated capability allows different spatial regions of the domain to be solved independently, enhancing the code’s efficiency, particularly when running large-scale simulations in complex domains. The scalability of this approach is demonstrated, highlighting its potential to transform how transients are approached in CFD simulations. Lastly, the report focuses on how this overlapping domain capability specifically accelerates transient simulations through multi-rate timestepping. By decoupling different regions and facilitating faster computations, this method offers a promising pathway to making nuclear transient simulations more computationally feasible, addressing one of the critical bottlenecks in the field. Together, these advancements represent a significant leap forward in transient simulation technology, bringing closer the possibility of handling highly complex nuclear scenarios with greater efficiency and accuracy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Carbon-Binder Weight Loading Optimization for Improved Lithium-Ion Battery Rate Capability

Battery performance is strongly correlated with electrode microstructure and weight loading of the electrode components. Among them are the carbon-black and binder additives that enhance effective conductivity and provide mechanical integrity. However, these both reduce effective ionic transport in the electrolyte phase and reduce energy density. Therefore, an optimal additive loading is required to maximize performance, especially for fast charging where ionic transport is essential. Such optimization analysis is however challenging due to the nanoscale imaging limitations that prevent characterizing this additive phase and thus quantifying its impact on performance. Herein, an additive-phase generation algorithm has been developed to remedy this limitation and identify percolation threshold used to define a minimal additive loading. Improved ionic transport coefficients from reducing additive loading has been then quantified through homogenization calculation, macroscale model fitting, and experimental symmetric cell measurement, with good agreement between the methods. Rate capability test demonstrates capacity improvement at fast charge at the beginning of life, from 37% to 55%, respectively for high and low additive loading during 6C CC charging, in agreement with macroscale model, and attributed to a combination of lower cathode impedance, reduced electrode tortuosity and cathode thickness.

25 ENERGY STORAGE↗

DOE OSTI2016

High-order shock-fitted detonation propagation in high explosives

In this paper, a highly accurate numerical shock and material interface fitting scheme composed of fifth-order spatial and third- or fifth-order temporal discretizations is applied to the two-dimensional reactive Euler equations in both slab and axisymmetric geometries. High rates of convergence are not typically possible with shock-capturing methods as the Taylor series analysis breaks down in the vicinity of discontinuities. Furthermore, for typical high explosive (HE) simulations, the effects of material interfaces at the charge boundary can also cause significant computational errors. Fitting a computational boundary to both the shock front and material interface (i.e. streamline) alleviates the computational errors associated with captured shocks and thus opens up the possibility of high rates of convergence for multi-dimensional shock and detonation flows. Several verification tests, including a Sedov blast wave, a Zel'dovich–von Neumann–Döring (ZND) detonation wave, and Taylor–Maccoll supersonic flow over a cone, are utilized to demonstrate high rates of convergence to nontrivial shock and reaction flows. Comparisons to previously published shock-capturing multi-dimensional detonations in a polytropic fluid with a constant adiabatic exponent (PF-CAE) are made, demonstrating significantly lower computational error for the present shock and material interface fitting method. For an error on the order of 10 m / s , which is similar to that observed in experiments, shock-fitting offers a computational savings on the order of 1000. In addition, the behavior of the detonation phase speed is examined for several slab widths to evaluate the detonation performance of PBX 9501 while utilizing the Wescott–Stewart–Davis (WSD) model, which is commonly used in HE modeling. It is found that the thickness effect curve resulting from this equation of state and reaction model using published values is dramatically more steep than observed in recent experiments. Utilizing the present fitting strategy, in conjunction with a nonlinear optimizer, a new set of reaction rate parameters improves the correlation of the model to experimental results. In conclusion, this new model is tested against two dimensional slabs as a validation test.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2018

The Variable Saturation Hydraulic Conductivity Method for Improving Soil Water Content Simulation in EPIC and APEX Models

Core Ideas We propose VSHC for calculating soil water percolation in continuous models. VSHC relates permeability to daily hydraulic properties to estimate hydraulic conductivity. VSHC performed well at field scale and improved estimates of peak streamflows. Soil water percolation is a key process in the life cycle of water in fields, watersheds, and river basins. The Environmental Policy Integrated Climate (EPIC) and the Agricultural Policy/Environmental eXtender (APEX) are continuous models developed for evaluating the environmental effects of agricultural management. Traditionally, these models have simulated soil water percolation processes using a tipping‐bucket approach, with the rate of flow limited by the saturated hydraulic conductivity. This simple approach often leads to inaccuracy in simulating elevated soil water conditions where soil water content (SWC) levels may remain above field capacity under prolonged wet weather periods or limited drainage. To overcome this deficiency, a new submodel, the variable saturation hydraulic conductivity (VSHC) method, was developed for simulating soil water percolation processes using a nonlinear equation to estimate the effective hydraulic conductivity as a function of the SWC and soil properties. The VSHC method was evaluated at three sites in the United States and two sites in Europe. In addition, a numerical solution of the Richards equation was used as a benchmark for SWC comparison. Results show that the VSHC method substantially improves the accuracy of the SWC simulation in long‐term simulations, particularly during wet periods. At the watershed scale, results on the Riesel Y2 watershed indicate that the VSHC method enhances model performance in the high‐flow regime of channel peak flows because of the improved estimation of SWC, which implies that the improved SWC simulation at the field scale is beneficial to hydrologic modeling at the watershed scale.

Doro, Luca↗

DOE OSTI2018

Rates of Ligand Exchange around the Bis-Oxalato Complex [NpO 2 (C 2 O 4 ) 2 ] 3- Measured by Using Multinuclear NMR Spectroscopy under Neutral to Semi-Alkaline Conditions [Rates of ligand exchange around the bis-oxalato complex NpO2(C2O4)23- measured using multinuclear NMR at neutral to semi-alkaline conditions]

The kinetics of ligand exchange between the free oxalate ion, C 2 O 4 2- , and the bis-oxalato Np V complex, [NpO 2 (C 2 O 4 ) 2 ] 3- , in aqueous solution are reported by using 13 C and 17 O NMR spectroscopy methods. Rates of exchange were measured in the pH regime of 6.5–9.0, at which speciation is shown to be suitably simple. Because the neptunium(V) complex is paramagnetic, the rates of ligand exchange were estimated by following the width of the 13 C and 17 O signals assigned to the free oxalate ion in solution and by applying the Swift–Connick method for measuring rates of exchange. A set of experiments were conducted in which pH and total oxalate concentration were varied, and the linear dependence of the rate on these parameters was demonstrated. Variable-temperature NMR spectroscopy was also performed to measure activation parameters of complexation. Finally, at pH<8.0, ΔH ≠ =16.9 ±4.9 kJ mol -1 and ΔS≠=-116.3 ±17.1kJ mol -1 K -1 , whereas at pH>8.0 there is almost no dependence on temperature, which is interpreted to indicate that hydrolysis is coupled to ligand exchange under these conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density

Improvements in wind energy technology, reduced costs, and ambitious clean energy goals have led to projections of high wind contribution in coming years. Developing methodologies to design wind plants with a variety of siting constraints and turbine sizes helps enable high wind penetration, and gain a better understanding of how wind plants are sensitive to setback constraints and turbine design. In this paper, we present a two-step optimization method to simultaneously determine the optimal number of turbines and their locations in a wind plant domain divided into many small, discrete parcels. We present the optimized performance metrics of a wind plant optimized with different turbine sizes and ratings, and with different siting restrictions within the wind plant. Our results indicate that taller and larger turbines are more sensitive to increasing siting constraints. We also compare the optimal wind plant layouts and performance for wind plants optimized for minimum COE and maximum profit. Wind plants optimized for profit had 130%-190% of the capacity of plants optimized for COE, which demonstrates that the optimal results are greatly affected by the objective function, which should be carefully considered. Finally, in this paper we demonstrate the effect of increasing siting constraints on wind plant capacity density, and how the results change when different land areas are used to calculate capacity density. When using the entire wind plant boundary area to determine capacity density, increasing siting constraints decreases the capacity density. However, when we only use the available area (the area left after removing the siting constraints) to calculate the capacity density, increasing the siting constraints increases capacity density. This is a critical insight because of how capacity density is typically defined and used in research, and has important implications for assessment of technical potential and capacity expansion modeling, as well as future wind deployment potential.

17 WIND ENERGY↗

DOE OSTI2022

Realistic operation of two residential cordwood-fired outdoor hydronic heater appliances—Part 2: Particle number and size

The use of wood as a fuel for home heating is a concern from an environmental health and safety perspective as biomass combustion appliances emit high concentrations of particulate matter. Wood burning significantly contributes to wintertime particulate matter concentrations in many states in the northern United States. Of particular concern are outdoor wood-fired hydronic heaters. These devices are concerning as they tend to have very large combustion chambers and typical use patterns can result in long periods of low output, which result in an increased particulate matter emission rate relative to high heat output operating conditions. In this study, the performance of two hydronic heaters operating under different combustion conditions, including four different heat output categories approximately corresponding to categories I–IV denoted in Environmental Protection Agency Method 28 Outdoor Wood-fired Hydronic Heaters, and during start-up and reloading events were investigated. Measurements of flue gas particulate number concentration and size for particles with aerodynamic diameters between 0.006 and 10 µm were made using a dilution sampling system. The measured particle number concentration in the flue gas was between 0.71 and 420 million particles per cubic centimeter and was dependent on fuel loading and heat output. For each hydronic heater tested, the highest average particle concentration was found at the beginning of each test during the cold-start condition. Additionally, the majority of the particles had aerodynamic diameters less than 0.100 µm (particles of this size made up between 64% and 97% of all particles) and less than 1% of all particles had aerodynamic diameters greater than 1 µm for all phases. For particles in the accumulation mode, between 0.100 and 1 µm, the mean particle diameter was dependent on fuel loading and heat output.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Network Detection of Radiation Sources Using Localization-Based Approaches

Radiation source detection is a critical problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple “aggregated” area as a result of localization being enabled by the networked detectors. We introduce the following three localization-based detection approaches: 1) source-attractor radiation detection (SRD); 2) triangulation-based radiation source detection (TriRSD); and 3) the ratio of square distance-based radiation source detection (ROSD-RSD). We use canonical datasets from Domestic Nuclear Detection Office's intelligence radiation sensors systems tests to assess the performance of these methods. Extensive results demonstrate that SRD outperforms TriRSD and ROSD-RSD, and other existing detection algorithms based on the sequential probability ratio test and maximum likelihood estimation in terms of both false alarm and detection rates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Deep Reinforcement Learning From Demonstrations to Assist Service Restoration in Islanded Microgrids

Microgrids can be operated in island mode during utility grid outages to support service restoration and improve system resilience. To schedule and dispatch distributed energy resources (DERs) in an islanded microgrid, conventional model-based methods rely on accurate distribution network models and lack generalization and adaptability. Data-driven methods are promising for DER coordination but face practical challenges such as potential hazards to microgrids during online training and insufficient online training opportunities due to low outage rates. This paper presents a novel two-stage learning framework that builds on the deep deterministic policy gradient from demonstrations to identify an optimal restoration strategy. At the pre-training stage, imitation learning is applied to equip the control agent with expert experiences to guarantee acceptable initial performance. At the online training stage, action clipping, reward shaping, and expert demonstrations are leveraged to ensure safe exploration while accelerating the training process. In conclusion, the proposed method is illustrated using the IEEE 123-node system and compared with a representative model-based method and the standard deep deterministic policy gradient method to prove solution accuracy and demonstrate increased computational efficiency.

Du, Yan↗

DOE OSTI2022

Correlations between Texture Profile Analysis and Sensory Evaluation of Cured Largemouth Bass Meat (Micropterus salmoides)

Texture is an important factor in evaluating the quality of aquatic products. To evaluate the texture properties of cured large mouth bass, edible sodium chloride (0, 1.0, 2.0, and 3.0%) was smeared to the bass meat. Texture profile analysis (TPA) and sensory evaluation were performed to evaluate the quality of the cured samples, and the correlations of the indexes in the two methods were analyzed. Two principal components were obtained from the TPA indexes and sensory evaluation indexes, and the cumulative variance contribution rates were 73.87% and 72.99%, respectively. Results from the principal component analysis showed that the main indicators that affected the TPA were gumminess and springiness, while those that affected sensory evaluation were chewiness and adhesiveness. The TPA index and sensory evaluation could be effectively improved when the sodium chloride added to the bass meat was 1%. In the correlation analysis, sensory springiness was negatively correlated with TPA hardness ( P < 0.05 , r = −0.553) but positively correlated with TPA chewiness ( P < 0.05 , r = 0.596). After stepwise regression analysis, the prediction equation between the sensory springiness and TPA hardness was obtained as SSp=5.770−0.002Ha. These results provide a basis for predicting the quality of large mouth bass cured products.

Li, Meijin↗

DOE OSTI2018

Virologic outcome among patients receiving antiretroviral therapy at five hospitals in Haiti

Introduction Viral load (VL) assessment is the preferred method for diagnosing and confirming virologic failure for patients on antiretroviral therapy (ART). We conducted a retrospective cross-sectional study to evaluate the virologic suppression rate among patients on ART for 6 months in five hospitals around Port-au-Prince, Haiti. Methods Plasma VL was measured and patients with VL <1,000 copies/mL were defined as virologically suppressed. A second VL test was performed within at least six months of the first test. Factors associated with virologic suppression were analyzed using logistic regression models accounting for site-level clustering using complex survey procedures. Results Data were analyzed for 2,313 patients on ART for six months or longer between July 2013 and February 2015. Among them, 1,563 (67.6%) achieved virologic suppression at the first VL test. A second VL test was performed within at least six months for 718 (31.0%) of the patients. Of the 459 patients with an initial HIV-1 RNA <1,000 copies/mL who had a second VL performed, 394 (85.8%) maintained virologic suppression. Virologic suppression was negatively associated with male gender (adjusted odds ratio [aOR]: 0.80, 95% CI: 0.74– 0.0.86), 23 to 35 months on ART (aOR:0.72[0.54–0.96]), baseline CD4 counts of 201–500 cells/mm 3 and 200 cells/mm 3 or lower (aORs: 0.77 [0.62–0.95] and 0.80 [0.66–0.98], respectively), poor adherence (aOR: 0.69 [0.59–0.81]), and TB co-infection (aOR: 0.73 [0.55–0.97]). Conclusions This study showed that over two-thirds of the patients in this evaluation achieved virologic suppression after six months on ART and the majority of them remained suppressed. These results reinforce the importance of expanding access to HIV-1 viral load testing in Haiti for monitoring ART outcomes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 5: Utah FORGE Well 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 4: Utah FORGE Well 78B-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 3: Utah FORGE Well 56-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 1: Summary of Utah FORGE Wells 16A(78)-32, 56-32, 78B-32 and 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗