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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Low Threshold Acquisition controller for Skipper CCDs

The development of the Skipper Charge Coupled Devices (Skipper-CCDs) has been a major technological breakthrough for sensing very weak ionizing particles. The sensor allows to reach the ultimate sensitivity of silicon material as a charge signal sensor by unambiguous determination of the charge signal collected by each cell or pixel, even for single electron-hole pair ionization. Extensive use of the technology was limited by the lack of specific equipment to operate the sensor at the ultimate performance. In this work a simple, single-board Skipper-CCD controller is presented, aimed for the operation of the detector in high sensitivity scientific applications. The article describes the main components and functionality of the Low Threshold Acquisition (LTA) together with experimental results when connected to a Skipper-CCD sensor. Measurements show unprecedented deep sub-electron noise of 0.039 e$^-_{rms}$/pix for 5000 pixel measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Higher-order phonon scattering: advancing the quantum theory of phonon linewidth, thermal conductivity and thermal radiative properties

Phonon scattering plays a central role in the quantum theory of phonon linewidth, which in turn governs important properties including infrared spectra, Raman spectra, lattice thermal conductivity, thermal radiative properties, and also signifi- cantly affects other important processes such as hot electron relaxation. Since Maradudin and Fein’s classic work in 1962, three-phonon scattering had been considered as the dominant intrinsic phonon scattering mechanism and has seen tremendous advances. However, the role of the higher-order four-phonon scattering had been persistently unclear and so was ignored. The tremendous complexity of the formalism and computational challenges stood in the way, prohibiting the direct and quantitative treatment of four-phonon scattering. In 2016, a rigorous four-phonon scattering formalism was developed, and the prediction was realized using empirical potentials. In 2017, the method was extended using first-principles calculated force constants, and the thermal conductivities of boron arsenides (BAs), Si and diamond were predicted. The predictions for BAs were later confirmed by several independent experiments. Four-phonon scattering has since been investigated in a range of materials and established as an important intrinsic scattering mechanism for thermal transport and radiative properties. Specifically, four-phonon scattering is important when the fourth-order scattering potential or phase space becomes relatively large. The former scenario includes: (i) nearly all materials when the temperature is high; (ii) strongly anharmonic (low thermal conductivity) materials, including most rocksalt compounds, halides, hydrides, chalcogenides and oxides. The latter scenario includes: (iii) materials with large acoustic–optical phonon band gaps, such as XY compounds with a large atomic mass ratio between X and Y; (iv) two- dimensional materials with reflection symmetry, such as single-layer graphene, single-layer boron nitride and carbon nanotubes; and (v) phonons with a large density of states, such as optical phonons, which are important for Raman, infrared and thermal radiative properties. Four-phonon scattering is expected to gain broad interest in various technologically important materials for thermoelectrics, thermal barrier coatings, thermal energy storage, phase change, nuclear power, ultra-high temperature ceramics, infrared spectra, Raman spectra, radiative transport, hot electron relaxation and radiative cooling. Four-phonon scattering has been, and will continue to be, established as an important intrinsic phonon scattering mechanism beyond three-phonon scattering. The prediction of four-phonon scattering will transition from a breakthrough to a new routine in the next decade.

Feng, Tianli↗
DOE OSTI2020

Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterizations

Machine learning applied to computer vision and pattern recognition is a type of artificial intelligence that has advanced rapidly in the last 10 to 15 years, spurred forward by breakthroughs in deep convolutional neural networks. These state-of-the-art methods are poised to become widely used in environmental monitoring applications as a result of the increasing abundance of data available from different imaging platforms (e.g., fixed-point cameras, drone surveys, high-resolution satellite data) that can be analyzed to observe, model, and understand environmental conditions. Computer vision and pattern recognition (CVPR) tools advance our ability to use imagery and camera-based tools in cost-effective ways for environmental monitoring. Although these techniques offer great potential, some challenges remain, such as the need for large sets of labeled images for model training and validation and optimized hardware and software to ensure that the models can be trained effectively and in a reasonable amount of time. Here, we have overcome many of these rate-limiting challenges by using a diverse image library built across multiple projects coupled with staff expertise and onsite computing resources. We are working toward a fully automated SPI processing system and also are moving to develop CVPR analytical tools for other imaging platforms and data sets.

02 PETROLEUM↗
DOE OSTI2020

Accomplishments and Mid-Year Performance Report: Wind Energy Program Fiscal Year 2020

Through transformative science and innovation, the U.S. Department of Energy's (DOE's) National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory's (NREL's) Flatirons Campus helps lead the way to a sustainable energy future that powers America with significant levels of reliable, low-cost, and accessible wind energy. Since 1976, NREL has provided an ideal environment for the research and development (R&D) of advanced energy technologies through: Wind energy resource assessments, World-class research facilities, High-performance computing and modeling, Data and technology analysis, Manufacturing breakthroughs, Environmental analysis and wildlife conservation efforts, Education and training programs for a future workforce. These activities enable the innovations needed to advance U.S. wind systems, address market and deployment barriers, and drive down the cost of wind energy with more efficient, more reliable, and more predictable wind energy systems. This report provides an overview of the achievements NREL delivered on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during the first and second quarters of Fiscal Year (FY) 2020.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗
DOE OSTI2020

Impact of sub-core scale heterogeneity on CO2/brine multiphase flow for geological carbon storage in the Minnelusa sandstone

CO2 geological storage in deep saline aquifers is a mitigation option for CO2 emissions due to its large storage capacity and immediate accessibility. Accurately determining the CO2-brine relative permeability curves is key to the evaluation of CO2 injectivity and sweep efficiency in reservoir simulation as well as the CO2 injection in the field. This study highlights the remarkable effects of sub-core scale heterogeneity on the CO2-brine multiphase flow properties of the Minnelusa Sandstone in Wyoming. Two unsteady state CO2-brine drainage experiments were performed on the two samples. The first sample exhibits slanted laminated structure, while the second one represents a more homogeneous sandstone system. The CO2 saturation distributions during drainage reveals that the main variation in multiphase flow properties of two core samples were attributed to the porosity distribution that leads to the capillary pressure heterogeneity. Assisted history matching was used to obtain the respective relative permeability curves, which suggests heterogeneity-dependent behavior. In addition, sensitive and uncertainty analyses indicate that physical and petro-physical properties of low-porosity and low-permeability bedding layers exert marked effects on CO2 front breakthrough time and brine production. The results presented in this study help to gain insight into the CO2-brine multiphase flow properties in heterogeneous sandstones and can pave the way for the upscaling of CO2 migration and field-scale simulation accurately. This work is funded under the Department of Energy CarbonSAFE program (awards DE-FE0031624 DE0031891).

Kou, Zuhao↗
DOE OSTI2021

One of These Things IS Like the Other: Pursuing a New Taxonomy of Industry for Improved Energy System Modeling

Industrial processes drive the exchange of materials, energy, and currency throughout the economy. These processes are powered by electricity and direct combustion, with variation in their operation even within the same industry. This heterogeneity makes it difficult for large models, including the National Energy Modeling System (US), to project their energy use while remaining tractable. Decarbonization and ensuing changes to the energy system require changes to industrial processes while offering opportunities for process innovation, but the extent and nature of changes are difficult to model with current classification schemes and corresponding data. The North American Industrial Classification (NAICS) is an economic taxonomy of industries, but its categories are less meaningful from an energy and material flow perspective. For example, a facility that makes steel from iron ore in a blast furnace/basic oxygen furnace is categorized under the same NAICS code as a facility that makes steel from scrap in an electric arc furnace despite the scale, use of recycled scrap versus iron ore, and energy use differences in the two facility types. Exploratory analysis is performed on a large dataset used for plant-level energy assessment in order to detect clusters that can aid in better modeling of industry for energy analysis in an evolving system with breakthrough technologies.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2021

Opportunities for DOE National Laboratory-led QuantISED Experiments

A subset of QuantISED Sensor PIs met virtually on May 26, 2020 to discuss a response to a charge by the DOE Office of High Energy Physics. In this document, we summarize the QuantISED sensor community discussion, including a consideration of HEP science enabled by quantum sensors, describing the distinction between Quantum 1.0 and Quantum 2.0, and discussing synergies/complementarity with the new DOE NQI centers and with research supported by other SC offices. Quantum 2.0 advances in sensor technology offer many opportunities and new approaches for HEP experiments. The DOE HEP QuantISED program could support a portfolio of small experiments based on these advances. QuantISED experiments could use sensor technologies that exemplify Quantum 2.0 breakthroughs. They would strive to achieve new HEP science results, while possibly spinning off other domain science applications or serving as pathfinders for future HEP science targets. QuantISED experiments should be led by a DOE laboratory, to take advantage of laboratory technical resources, infrastructure, and expertise in the safe and efficient construction, operation, and review of experiments. The QuantISED PIs emphasized that the quest for HEP science results under the QuantISED program is distinct from the ongoing DOE HEP programs on the energy, intensity, and cosmic frontiers. There is robust evidence for the existence of particles and phenomena beyond the Standard Model, including dark matter, dark energy, quantum gravity, and new physics responsible for neutrino masses, cosmic inflation, and the cosmic preference for matter over antimatter. Where is this physics and how do we find it? The QuantISED program can exploit new capabilities provided by quantum technology to probe these kinds of science questions in new ways and over a broader range of science parameters than can be achieved with conventional techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Accomplishments and Year-End Performance Report; Wind Energy Program: Fiscal Year 2021

The National Wind Technology Center (NWTC), located at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) Flatirons Campus, has been a driving force in advancing wind energy technology research worldwide since its designation as a DOE national research center in 1992. Enabled by the Flatirons Campus's world-class facilities, scientists, engineers, analysts, and researchers are pushing the frontiers of science to pursue wind energy innovation. In Fiscal Year (FY) 2021, NREL continued to provide the technical expertise, research capabilities, and industry understanding to support DOE's ambitious climate action and research goals by advancing technology, addressing market and deployment barriers, and driving down costs with more efficient, reliable, and predictable wind energy systems. One of several highlights, NREL received an R&D 100 Special Recognition Award for its Thermoplastic Resin System for Wind Turbine Blades. This breakthrough in the wind turbine manufacturing process will enable the production of recyclable blades that are stronger, longer, and less expensive, while increasing energy capture, decreasing energy and transportation costs, and increasing blade reliability. In a year when the entire U.S. economy struggled to address workforce gaps, an NREL study compared wind industry needs, training programs, and hiring practices with perspectives from students and recent college graduates. Researchers hope that, by pinpointing areas of disconnect, the expectations of employers who have difficulty filling entry-level jobs can better align with the preparation of the potential applicants who find it hard to break into the field. The lab also made numerous new data and modeling resources available in FY 2021. Recent NREL releases include a modeling tool for predicting the power performance and structural loads of wind turbines within a wind farm (FAST.Farm), a computational framework for modeling golden eagle behavior near wind farms, and 20 years of offshore wind data. Updates were also made to the widely used Wind Plant Integrated Systems Design and Engineering Model (WISDEM), which couples engineering and cost models to examine system-level trade-offs. Now, bolstered by a renewed national commitment to tackle climate change and revitalize the U.S. economy through increased investment in clean energy - particularly in offshore wind energy - NREL stands poised to lead the way to a sustainable future that powers the United States with significant levels of reliable, low-cost, accessible wind energy. This report provides an overview of the achievements NREL made on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during FY 2021 (between Oct. 1, 2020, and Sept. 30, 2021).

Flatirons Campus↗
DOE OSTI2021

High Power Superconducting Electron Linear Accelerators For Industrial Application

Fermilab is executing a technology development program to develop a compact yet powerful electron accelerator. We are leveraging R&D breakthroughs in SRF cavities, cost-effective radio-frequency sources, modern cryo-coolers, and high average current electron guns. We show that a single accelerator module can deliver average beam power as high as 1 MW through detailed thermal, RF, and particle simulations.

Thangaraj, Jayakar Tobin↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2022

Scintillation Hydro-Gel for Isotopic Neutron (SHINE): Eco-Friendly Quantum Dot Neutron Detectors

The development of new neutron detectors to replace helium-3 (3He) detectors is imperative due to a worldwide shortage of 3He following the draw down in nuclear weapons production since the end of the Cold War. The United States Department of Homeland Security would like to deploy monitors for the detection of neutron emissions from shipping containers housing illicit nuclear material; however, this effort has been put on hold until new replacements for 3He detectors can be developed. Scintillation Hydro-Gel for isotopic Neutron Emitters (SHINE) is a unique, first of its kind, 6Li-loaded quantum dot gel scintillator developed at INL. By incorporating 6Li with quantum dots in a gel matrix, SHINE displays the best properties of liquid and solid scintillators without their disadvantages such as continuous filtering to keep liquids free of contaminates, slow throughput of containers, higher base component costs, ‘dead’ voids in solid scintillators, and a high loading of 6Li without compromising on light transparency. Additionally, SHINE is completely eco-friendly, a breakthrough in high-efficiency detection systems. SHINE is a unique combination of 6LiCl, a highly water-soluble compound, and InP/ZnS core/shell quantum dots, which are poured into a gel-form using cross-linking polymers. In this presentation, SHINE has been successfully tested for neutron detection and shows promise as both a replacement for current 3He neutron detectors as well as potential use in handheld, compact neutron detection units and antineutrino detection.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantum computing hardware for HEP algorithms and sensing

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Experimental Investigation of Polymer Induced Fouling of Heater Tubes in the First-Ever Polymer Flood Pilot on Alaska North Slope

Mineral fouling in heat exchangers has been extensively investigated by researchers in recent times. The oil and gas industry has a long history of fouling issues in production systems as a result of produced fluids treatment. Due to decline in production rates in oilfields new technologies are being developed and field tested in pilots. Polymer flooding is one such technology that involves addition of polymers to injection fluids to enhance oil production. A polymer flood pilot has been set up in the Schrader Bluff viscous oil reservoir at Milne Point field on the Alaska North Slope (ANS). The results from the pilot are encouraging, however a major concern of the operator is the influence of polymer on the production system after breakthrough, especially the fouling in heat exchangers. This study investigates the propensity of polymer fouling on the heater tubes as a function of different variables, with the ultimate goal of determining safe and efficient operating conditions. This work applies a multi-experimental approach to study the severity of polymer-induced fouling in both dynamic and static states of produced fluids as well as studying the stability of polymer solutions at different temperatures. A unique experimental setup was designed and developed in-house to simulate the fouling process on the heating tube. The influence of heating tube skin temperature, tube material, and polymer concentration on fouling tendency was investigated. Each test was run five times with the same tube, and in each run, the freshly prepared synthetic brine and polymer solution was heated from 77°F to 122°F to mimic field-operating conditions. The heating time and fouling amount were recorded for each run. Dynamic Scale Loop (DSL) tests were conducted to study fouling due to polymer at different temperatures (165°F to 350°F) in a dynamic state of fluid flow where the fluids mimic the residence time of fluids in the heat exchanger on the field pilot. Cloud point measurement has also been conducted to find the critical temperature at which the polymer in solution becomes unstable and precipitates out. The morphology and composition of the deposit samples were analyzed by environmental scanning electron microscopy (ESEM) and X-ray diffraction (XRD), respectively. It was found that the presence of polymer in produced fluids would aggravate the fouling issues on both carbon steel and stainless-steel surfaces at all tested skin temperatures. Only higher skin temperatures of 250°F and 350°F could cause polymer-induced fouling issues on the copper tube surface, and the fouling tendency increased with polymer concentration. At the lower skin temperatures of 165°F, no polymer-induced fouling was identified on the copper tube. A critical temperature that is related to the cloud point of the polymer solution was believed to exist, below which polymer-induced fouling would not occur, and only mineral scale was deposited but above which the polymer would aggravate the fouling issue. The cloud point of the tested polymer solution was determined to be between 220°F and 230°F. In the DSL tests it was found that at higher skin temperatures of 250°F and 350°F tube blocking was observed in the DSL tests whereas the tests at 165°F and 200°F did not show any tube blocking in the same time period. These experiments also manifested the influence of cloud point of the solution as deposit rate increased significantly in both carbon steel and stainless-steel tubes when the skin temperature was higher than the solution cloud point. The results of this study have provided guidance to the operator for the field-operations.

Dhaliwal, Anshul↗
DOE OSTI2022

Technical and Economic Evaluation of the First Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska's North Slope via Machine Assisted History Matching

Polymer flooding has become globally established as a potential enhanced oil recovery method for heavy oils. To determine whether this technology may be useful in developing the substantial heavy oil resources on the Alaska North Slope, a polymer flood field pilot commenced at the Milne Point Unit in August 2018. This study seeks to evaluate the results of the field pilot on a technical and economic basis. A reservoir simulation model is constructed and calibrated to predict the oil recovery performance of the pilot through machine-assisted reservoir simulation techniques. To replicate the early water breakthrough observed during waterflooding, transmissibility contrasts are introduced into the simulation model, forcing viscous fingering effects. In the ensuing polymer flood, these transmissibility contrasts are reduced to replicate the restoration of injection conformance during polymer flooding. Transmissibility contrasts are later reinstated to replicate fracture overextension interpreted in one of the producing wells. The calibrated simulation models produced at each stage of the history matching process are used to forecast oil recovery. These forecasts are used as input for economic analysis, incremental to waterflooding expectations. The simulation forecasts indicate that polymer flooding significantly increases the heavy oil production for this field pilot compared to waterflooding alone, yielding attractive project economics. However, meaningful variations between simulation scenarios demonstrate that a simulation model is only valid for prediction if flow behavior in the reservoir remains consistent with that observed during the history matched period. Critically, this means that a simulation model calibrated for waterflooding may not fully capture the technical and economic benefits of an enhanced oil recovery process such as polymer flooding. Subsequently, the simulation model and economic model are used in conjunction to conduct a sensitivity analysis for polymer flood design parameters, from which recommendations are provided for both the continued operation of the current field pilot and future polymer flood designs. The results demonstrate that a higher polymer concentration can be injected due to the development of fractures in the reservoir. The throughput rate should remain high without exceeding operating constraints. A calculated point-forward polymer utilization parameter demonstrates the decreasing efficiency of the polymer flood at later times in the pattern life. Future projects will benefit from starting polymer injection earlier in the pattern life. A pattern with tighter horizontal well spacing will observe a greater incremental benefit from polymer flooding.

Keith, Cody↗
DOE OSTI2021

Enhanced heavy oil recovery by low salinity polymer flood combined with microgel treatment

“Heavy oil resources account for a large portion of the total oil reserves around the world. The target heavy oil reservoir is located on Alaska’s North Slope (ANS). Advantages of low-salinity HPAM polymer (LSP) over high-salinity polymer (HSP) were demonstrated. LSP could recover more oil with 40% less polymer consumption. No additional oil was recovered by HSP after LSP flood. The first-ever polymer flood pilot on ANS showed remarkable success regarding water cut reduction, oil production increase, delayed breakthrough, and projected oil recovery improvement. Polymer alone was insufficient to achieve satisfactory recovery as the reservoirs were highly heterogeneous. Microgels could improve the effectiveness of polymer flood by reducing water cut and increasing oil recovery. Favorable working conditions were identified. Microgel transport behavior was studied using superpermeable sandpacks (27-221 darcies) with multiple pressure sensors. The particle-to-pore matching size ratio significantly impacted the effectiveness of the gels. A threshold differential pressure (ΔPth) and critical pressure gradient (∇Pcr) were required to push the gels to penetrate and propagate through the channels. The ΔPth and ∇Pcr revealed the underlying mechanisms of selective penetration/placement behavior of microgels in heterogeneous reservoirs. Diagrams were developed to estimate the maximum propagation distance of the gels in channels in conceptual field applications. Sandwich-like channel models and methodologies were developed to comprehensively evaluate the effectiveness of gel materials. Gel retention in the channels was quantified. Results also indicated that the retained gels were dehydrated. Fluid diversion and sweep improvement after gel treatments were evaluated by tracer tests”--Abstract, page iv.

Zhao, Yang↗
DOE OSTI2022

Enhanced Geothermal Shot: Unlocking the Power of Geothermal Energy

The U.S. Department of Energy’s (DOE) Energy Earthshots Initiative aims to accelerate breakthroughs of more abundant, affordable, and reliable clean energy solutions. Achieving the Energy Earthshots targets will help America break down the biggest remaining scientific and technical barriers to tackling the climate crisis and reach the Biden-Harris Administration’s goal of net-zero carbon emissions by 2050 while creating good-paying union jobs and growing the economy.

Earthshot, Enhanced Geothermal Shot↗
DOE OSTI2022

Energy I-Corps Annual Report 2022

The U.S. Department of Energy Office of Technology Transitions is pleased to provide an update on the Energy I-Corps program. Now in its eighth year, Energy I-Corps addresses critical gaps in workforce development for our National Lab researchers. It provides meaningful real-world opportunities for the application of commercialization and entrepreneurial skills to DOE technologies. As of November 2022, 191 teams from 12 National Labs have participated in Energy I-Corps over the course of 15 cohorts and the pilot. Since its inception, Energy I-Corps participants have learned from more than 180 industry mentors and conducted over 13,600 discovery interviews with companies like: EPRI, Shell, Ford, World Bank, Breakthrough Energy, John Deere, Siemens Gamesa, Chevron, Eaton, Samsung, Lowes, Johns Manville, LEGO, U.S. Army, Trane, Tesla, GM, Dow Chemical, 3M, Whirlpool, GE, Home Depot, and Amazon. Over 20 teams have launched new businesses based on their Energy I-Corps technology. Post Energy I-Corps, technologies have attracted over $140M in post-program funding and executed over 75 licenses.

cohort↗