Search NASA⌕ Search

SEARCH · Search NASA

Results for “Field Operators”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

BioWatch Sample Management System (SMS) Field Operator Guide

The BioWatch Sample Management System is software developed by Los Alamos National Laboratory to support the Department of Homeland Security BioWatch program. Field Operators use the software to keep track of samples and to provide information on the sample management process to other users and software programs within BioWatch. The information tracked about a sample includes the date and time the filter media is assembled in a filter holder, deployed to the field, placed in and removed from a collector, and received from the field, and identifies who performs each task. Field Operators can also use the software to prepare and print forms to document the chain-of-custody transfer of the samples. The BioWatch SMS Field Operator Guide provides easy-to-understand procedures for each task a Field Operator performs. This guide gives basic information about using the computer and other equipment, instructions about checking and editing data, and provides reference information, including appendices of error messages and solutions, words used interchangeably, and safety warnings plus a glossary and an index.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Irreducibility of the set of field operators in NC QFT

Irreducibility of the set of quantum field operators has been proved in noncommutative quantum field theory in the general case when time does not commute with spatial variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Sequim Bay Underwater UXO Prototype Demonstration Site: Field Operations Summary, 2020

This report provides a summary of Pacific Northwest National Laboratory’s (PNNL’s) activities to initiate Phase II development of the Sequim Bay Underwater UXO Prototype demonstration site (MR-2735) in 2020. Testbed development and field operations were conducted by PNNL’s Coastal and Marine Research Laboratory (MCRL) in support of the Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) underwater Munitions Response (MR) program. A series of standardized underwater unexploded ordnance (UXO) demonstration sites (“testbeds”) are being developed by SERDP/ESTCP to test, evaluate, and demonstrate technologies that can detect, geolocate, and classify proud and buried munitions in 0–35 m water depths. As a prototype demonstration site, Sequim Bay is a semi-enclosed marine waterbody containing sand and mud sediments with minimal clutter in 5–30 m water depths. MCRL is located at the entrance to Sequim Bay, where it provides operational, logistical, and facilities support for testbed activities.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗
DOE OSTI2025

Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

"Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test"

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Jang, Kathy↗
DOE OSTI2024

Design, performance, and field operation of twisting heliostats for 3,000-sun concentration and high temperature

The idea is to combine the solar energy reflected from a field of many heliostats to obtain a single focus of high concentration and high power. We exploit a new type of “twisting” heliostat” in which as the mount is moved to track the sun through the day, the reflector shape is twisted to maintain a focused image of the sun’s disc on the target, through the day over a wide range of angles of incidence. By combining the light reflected by many twisting heliostats into a single focus, we are not only able to accomplish but also maintain very high concentration and temperature through the day, with higher efficiency than has previously been possible with conventional heliostats having a fixed shape. A circular field of twisting heliostats is used to power a single intense focus atop a central tower. The light from all the heliostats is relayed to an upward facing receiver at the focus via a central Cassegrain secondary reflector located above the receiver. In a specific design targeting 1 MW of power, a 100 m diameter field arrays 431 twisting heliostats, each with a 7 m2 reflector. The secondary reflector ,7.2 m in diameter, is located 24 m above the heliostat field, bringing sunlight with annual average power of 1 MW to a circular focus 0.8 m in diameter, at a concentration averaging 3,000 suns.

14 SOLAR ENERGY↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Integration of Clean Energy into Oil Field Operations

Integrating clean energy into oil and gas operations could reduce emissions and maximize higher-value use of produced hydrocarbons. In this published study, analysts from the Joint Institute for Strategic Energy Analysis (JISEA) and the National Renewable Energy Laboratory (NREL) evaluated clean power technologies for an oil field in the Delaware Basin using NREL’s Renewable Energy Integration and Optimization, or REopt, tool. The analysts evaluated different configurations of distributed energy resources based on the technologies available and the load they could satisfy, available land, and hypothetical carbon pricing. The analysis is part of a collaborative program with industry to understand site-specific energy consumption and prices in the oil and gas supply chain and determine under what conditions clean energy options are economically attractive. This work was sponsored by a consortium including ConocoPhillips, Baker Hughes, Extraction Oil & Gas, Kinder Morgan, and the Interstate Natural Gas Association of America Foundation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Treatability Study Pilot Test Operation Field Photos

Photos in each group are in chronological order as captured: Group I Tank Platform Setup, November 14, 2017; Group II Tank Setup, November 15, 2017; Group III Aboveground Injestion System (AIS) Setup, November 20, 2017; Group IV Chemical Mixing, November 21, 2017; Group V KB-1 Bacteria Injection, November 27, 2017; Group VI Miscellaneous.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Sequim Bay Underwater UXO (SBU2) Prototype Demonstration Site: Field Operations Summary, 2019

The U.S. Department of Defense (DoD) has identified more than 400 underwater sites that are potentially contaminated with munitions. Many of these sites are in shallow water (0–35 m deep) where the munitions pose a threat to human health and the environmental. To address this challenge, the Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) held a workshop in 2018 to establish the requirements and framework for developing a series of underwater unexploded ordnance (UXO) Standardized Demonstration sites (“test beds”) to support the evaluation of sensors, equipment, and technologies to detect UXO. Sequim Bay in Washington State was evaluated as a potential test bed and was determined to meet initial qualifying criteria with respect to environmental setting, and operational, logistical, and facilities support provided by the Pacific Northwest National Laboratory (PNNL).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2015

Groundwater remediation at the Fernald preserve, Cincinnati, Ohio modeling and resulting 2014 well field operational changes - 15324

The Fernald Preserve became a US DOE Office of Legacy Management site in November 2006, for long-term surveillance and maintenance following completion (with the exception of completing groundwater cleanup) of the Comprehensive Environmental Response, Compensation, and Liability Act; environmental remediation; and site restoration. When the site was turned over to the Office of Legacy Management, approximately 76 hectares (189 acres) of the Great Miami Aquifer (GMA) remained contaminated with uranium above the final remediation level of 30 micrograms per liter. Uranium contamination is being removed from groundwater in the GMA through a pump-and-treat operation that is predicted by groundwater modeling to continue until 2033. Certifying that cleanup objectives have been met and removing the remediation infrastructure after pump-and-treat (P and T) operations are complete will require a minimum of 3 additional years. The current P and T remedy has operated well. From August 1993 through December 2013 the remedy has been used to remove 5,345 kg (11,784 lb) of uranium from the GMA. A variety of metrics are used to track how the P and T operation is progressing and are reported each year in the Site Environmental Report for the Fernald site. The metrics show that the effectiveness of uranium removal is slowly decreasing (a common observation for P and T remedies) and that operational changes needed to be considered. Several different operational changes were modeled to determine if model-predicted cleanup dates could be shortened and if the effectiveness of the groundwater remediation could be improved. With the concurrence and support of US EPA, Ohio EPA, and site stakeholders, operational changes were agreed upon and implemented in 2014 that are predicted to increase the effectiveness of the ongoing operation and shorten cleanup times for a portion of the aquifer. Although the overall predicted length of the remediation was not shortened, modeling predicts that key areas of the uranium plume will be re-mediated sooner. This paper provides a status of the groundwater remediation under way at the Fernald Preserve near Cincinnati, presents the operational changes that were implemented in 2014, and summarizes the groundwater modeling that supported the changes. This paper also illustrates the potential benefits that can be gained by pro-actively managing a pump-and-treat operation and looking for innovative ways to adjust the operation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Magnetic moments of A = 3 nuclei obtained from chiral effective field theory operators

Chiral effective field theory (χEFT) provides a framework for obtaining internucleon interactions in a systematically improvable fashion from first principles, while also providing for the derivation of consistent electroweak current operators. In this study, we apply consistently derived interactions and currents towards calculating the magnetic dipole moments of the A = 3 systems 3 H and 3 He. We focus here on LENPIC interactions obtained using semilocal coordinate-space (SCS) regularization. Starting from the momentum-space representation of the LENPIC χEFT vector current, we derive the SCS-regularized magnetic dipole operator up through next-to-next-to-leading order (N 2 LO). We then carry out no-core shell-model calculations for 3 H and 3 He systems using the SCS LENPIC interaction at N 2 LO in χEFT and evaluate the magnetic dipole moments obtained using the consistently derived one-nucleon and two-nucleon electromagnetic currents. As anticipated by prior results with χEFT currents, the current corrections through N 2 LO provide improved, but not yet complete, agreement with experiment for the 3 H and 3 He magnetic dipole moments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Apparatus for performing oil field laser operations

A system, apparatus and methods for delivering high power laser energy to perform laser operations in oil fields and to form a borehole deep into the earth using laser energy. A laser downhole assembly for the delivery of high power laser energy to surfaces and areas in a borehole, which assembly may have laser optics and a fluid path.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2026

Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin (Final Technical Report)

This is the Final Technical Report for the project "Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin." Over the course of six years of collaboration between Battelle and project partners, all stated objectives of the program have been completed, including full geological characterization of the TBR trend (See companion report for Task2), laboratory and modeling experiments to determine the optimum composition and design of CO 2 -EOR operations in the TBR trend, execution of a field test of chemically-enhanced CO 2 in a TBR well, and integration of the data and learnings gathered during these efforts into a full-trend development plan. Detailed reporting on these activities, their outcomes, and implications for trend-wide development is provided in the report. This report and encompassed data will provide TBR field operators with detailed information on what worked, what did not work, and how to proceed with production optimization of their TBR assets using chemically-enhanced CO 2 -EOR. CO 2 -EOR is a relatively well understood and broadly implemented strategy for increasing incremental production across the oil and gas industry, but its application has been primarily focused on reservoirs with limited heterogeneity. The intention of this project was show first that the same physical mechanisms that improve recovery factors in homogeneous reservoirs (namely wettability alteration, viscosity alteration, oil swelling, and mobility control) are at play in heterogeneous reservoirs. This was proven by the project’s laboratory studies and dynamic simulations, with the potential exception of mobility control, which needs further study. The second intention was to demonstrate via direct field testing that CO 2 -EOR can work in a strongly heterogeneous reservoir. While the field test strategy implemented during this project did not succeed in producing oil, data gathered during the test sheds light on what may work for field operators who try chemically-enhanced CO 2 -EOR within their own reservoirs, significantly reducing the level of uncertainty carried by first-of-a-kind commercial efforts that could (and should) follow this test. Simultaneously, the project has identified several large-volume ethanol plants and other sources of CO 2 emissions in the region and provided a handrail that CO 2 emitters and field operators can leverage to capture, transport, and inject that CO 2 into their fields. This project has also shown that, in many cases, the economics of CO 2 -EOR in the TBR are attractive. And finally, by completing a project of this scope in the southern Michigan Basin, the project has contributed to the knowledge base and operational experience of field operators, state regulatory agencies, local service companies, and state universities, with CO 2 -EOR projects which should allow follow-on projects to proceed safely and efficiently.

02 PETROLEUM↗