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At least 37 records · Page 2

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↗

Using Earth-based Operational Field Tests as High-Fidelity Analogs for Planetary Surface Exploration

NASA is preparing to land the first woman and first person of color on the Moon within the next decade and establish a permanent sustainable human presence before sending humans onto Mars. To ensure the success of these missions, NASA has performed operational testing in terrestrial, aquatic, and laboratory analog environments that simulate Lunar and Martian environmental characteristics to evaluate exploration concepts of operations (ConOps), engineering design requirements, science support needs, mission operations techniques, and crew training. Terrestrial analogs include Desert Research and Technology Studies (D-RATS), Biologic Analog Science Associated with Lava Terrains (BASALT), and Next Space Technologies for Exploration Partnerships (NextSTEP)Habitat Ground Testing. Aquatic analogs include NASA Extreme Environment Mission Operations (NEEMO) and Pavilion Lake Research Project (PLRP).Laboratory analogs include the Neutral Buoyancy Laboratory (NBL), Active Response Gravity Offload System(ARGOS), rock yards, and virtual and hybrid reality simulation environments. While no single Earth-based analog environment is perfect for simulating all characteristics of other planetary surfaces, testing across multiple locations leverages the strengths of each to provide an integrated understanding of how to best conduct real spaceflight surface exploration missions.

B A Janoiko↗

Using Earth-based Operational Field Tests as High-Fidelity Analogs for Planetary Surface Exploration

NASA is preparing to land the first woman and first person of color on the Moon within the next decade and establishing a permanent sustainable human presence before sending humans onto Mars. To ensure the success of these missions, NASA has performed operational testing in terrestrial, aquatic, and laboratory analog environments that simulate Lunar and Martian environmental characteristics to evaluate exploration concepts of operations (ConOps), engineering design requirements, science support needs, mission operations techniques, and crew training.

B. A. Janoiko↗

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↗

Second Line of Defense Program, Secondary Screening Operations Field Data Collection

Secondary screening is a key component of the Second Line of Defense (SLD) Program’s capability to deter, detect, and interdict the illicit trafficking of radioactive materials. The primary detection system—the radiation portal monitor—makes alarm decisions based on gross counts of gamma and neutron radiation; it is able to provide little other information regarding what may have caused an elevated radiation level. Conducting a proper secondary screening with an instrument capable of identifying radioisotopes is the only way to determine what radioactive material is present—most importantly if it is a threat material. Only by knowing the radioactive material can a prudent decision be made with regard to disposition and response—and can the mistaken release of a threat be avoided.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

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↗

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↗

Localization of Virtual Objects in the Near Visual Field (Operator Interaction with Simple Virtual Objects)

Errors in the localization of nearby virtual objects presented via see-through, helmet mounted displays are examined as a function of viewing conditions and scene content in four experiments using a total of 38 subjects. Monocular, biocular or stereoscopic presentation of the virtual objects, accommodation (required focus), subjects' age, and the position of physical surfaces are examined. Nearby physical surfaces are found to introduce localization errors that differ depending upon the other experimental factors. These errors apparently arise from the occlusion of the physical background by the optically superimposed virtual objects. But they are modified by subjects' accommodative competence and specific viewing conditions. The apparent physical size and transparency of the virtual objects and physical surfaces respectively are influenced by their relative position when superimposed. The design implications of the findings are discussed in a concluding section.

Ellis, Stephen R.↗