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Performance Evaluation of Augmented Teleoperation of Contact Manipulation Tasks

At Argonne National Laboratory, an enhanced telerobotic concept has been suggested by providing augmented virtual fixtures for precise and dexterous manipulation. The proposed technology incorporated a human-robot interface that can assist personnel on the processes of Deactivation and Decommissioning in sites of high radiation. It has been implemented on a dual-arm robot system, known as Baxter, by integrating the Robotic Operating System (ROS) with 3D sensors and visual-haptic operator interface. However, what remained before its practical utilization is the capability for precisely placing the virtual fixtures. To this end, the objectives involved camera calibration, which required hardware fabrication and setup, as well as modification and implementation of ROS. The goal of this Research and Development is to develop an augmented-reality- based operator interface for human-inclusive, intelligent, and autonomous robotic system for the Deactivation and Decommissioning (D and D) of radioactive sites. The objectives are to develop: Collaborative Robot; Multi-modal Mixed-Reality Human-Robot Interface; Intelligent and Autonomous Behaviors. In order to achieve this, Baxter's Head-Kinect camera and Left-Hand camera need to be calibrated to overlap the real environment with the simulated environment. Camera calibration, which is crucial for precisely placing the virtual fixtures, was successful after running the simulations. The real and simulated environments overlapped, permitting the utilization of augmented teleoperation and the dexterous manipulation of Baxter for D and D applications in nuclear sites. Future steps: Performance evaluation of augmented teleoperation of contact manipulation tasks: Multiple-Camera 3D Reconstruction and Automation. Oculus Interface: Augmented reality and machine learning for enhanced telerobotic operation (Augmented Tele-autonomy): Machine-learning techniques for complex and flexible autonomous robotic behaviors. Interactive remote operation of robotic systems. Mesh cutting with simulation and real robot.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Conclusions from 3 years of continuous capture plant operation without exchange of the AMP/PZ-based solvent at Niederaussem – insights into solvent degradation management

A many times heard mantra of solvent degradation management in amine-based post combustion capture is “keep the solvent clean” to minimize solvent consumption. It is assumed that the amine losses would decrease by the removal of metals, degradation products, and reactive trace components which are captured from the flue gas, like NO 2 (as potentially driving components of the amine degradation besides dissolved O 2 ). However, this theoretical hypothesis – based on results from laboratory experiments typically generated with fresh amines – disregards the complexity of the solvent matrix, interaction of potential metal catalysts with degradation products and oxidizing agents, and specific chemical requirements which must be fulfilled before a degradation mechanism can proceed. Degradation of the solvent CESAR1 (aqueous solution of 3.0 molar 2-amino-2-methylpropan-1-ol (AMP) and 1.5 molar piperazine (PZ)) is investigated in a unique long-time test campaign (testing time up to now 40 months; 24/7 operation) without replacement of the solvent inventory at the capture pilot plant at the lignite-fired power plant in Niederaussem. Three solvent management strategies with different effect mechanisms are investigated and evaluated: (a) removal of only anionic compounds and trace elements (within 75 days solvent inventory treated two times) and anionic as well as cationic compounds and trace elements (114 days, inventory treated four times) from the solvent by ion exchange, (b) adsorptive removal of trace elements from the solvent by active carbon in 35% of the operating time, and (c) removal of >80% NO 2 by flue gas pretreatment with thiosulfate/sulfite solution (dosing for 2,000 h). The results of the testing program clearly show that “solvent cleanliness” is not a well-defined parameter and that results from laboratory tests, tests without fully representative industrial flue gasses, and short-term testing of monoethanolamine cannot be generalized for other solvents and industrial application. Furthermore, these results showcase that specific degradation management considering solvent, capture plant and flue gas quality is reasonable. Overshooting efforts for solvent management are contra-productive and produce unnecessary waste streams, efficiency losses and costs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗

OpenFacadeControl: enabling integration of automated facades with other building systems

Automated facades are, for the most part, still considered as separate from other building systems throughout the design, installation, commissioning, operation, and maintenance cycle. This takes place despite the fact that their energy and comfort performance are deeply interlinked with the operation of lighting and HVAC systems. Over the last two decades, research has shown that there are significant advantages from operating facades as an integrated system with the rest of the building. Nevertheless, significant barriers prevent this type of integration becoming more common. One of them is the lack of a platform that is inexpensive to implement and that easily allows the practical implementation of integrated control algorithms across fenestration and other building systems, using a variety of communications protocols. This is particularly challenging when automated facades are installed in existing buildings, where interaction with legacy building systems that were installed over the past lifetime of the building can require a high degree of interoperability. OpenFacadeControl (OFC) is an open-source controls framework aimed at unified control of facades and other building systems, including the sharing of third-party sensor information. Through leveraging the Volttron controls platform, it allows the integration of systems and sensors that are manufactured by different companies and that use different communications protocols into an ensemble that functions as a single system. OFC is designed to enable integrated control algorithms of varying degrees of complexity, ranging from simple, heuristic controls to more sophisticated approaches like model-predictive control. Use of a research version to test advanced lighting and shading strategies in a full-scale experimental testbed has demonstrated the ease of deploying advanced control solutions using OpenFacadeControl. This paper presents the structure of OpenFacadeControl and a demonstration case showing the use of OFC in laboratory tests of advanced lighting and fenestration controls that coordinated motorized shades communicating via the BACnet building communications standard and lights communicating via internet-protocol-based application programming interface (API), based on the readings of a shared light level sensor communicating via a different API.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗

The Transactive Energy Network Template Metamodel

While transactive energy, which is defined as an allocation of electricity based on dynamically discovered values or prices, has been extensively studied, its uptake and use has been slow. This report describes a tool, the transactive network template, which should hasten the creation and uptake of transactive energy networks. Some basic principles of transactive energy are familiar from existing wholesale electricity markets. Locational prices are calculated today for zones within bulk electric transmission systems. Locational prices differ while accounting for the locational costs of electricity generation and the losses and constraints incurred when electricity is transmitted from generators and distributed to consumers. A transactive energy network might include these transmission zones. However, current research strives to apply transactive energy also in electricity distribution circuits, buildings, and even for individual generating and consuming devices. At the same time, researchers explore how to apply transactive energy in real time during increasingly shorter time intervals. Automated computational agents become necessary as transactive energy becomes applied to smaller circuit zones and at faster dynamic timescales. A transactive energy network is an example of a multi-agent system. Each zone in the network is represented by its transactive agent, which makes decisions for and acts on behalf of a business entity that is responsible for and manages one of the circuit regions. A transactive energy network is also an example of a decentralized, distributed control system. Control decisions and responsibilities are distributed among the network’s transactive agents. The transactive agents are independent; that is, there typically is no centralized authority or oversight function. Instead, transactive agents exchange transactive signals and thereby negotiate the prices and quantities of electricity that they will exchange. Initially, the circuit regions and responsibilities of transactive agents appear to be very dissimilar. Each circuit region may comprise transmission, distribution, or building-level circuits. Each has a unique position and electrical connectivity within the transactive energy network. Each possesses unique assets that either generate or consume electricity, and these (e.g., renewable energy generator, diesel generator, aggregate utility load, building load, space conditioning, refrigerator, etc.) may further differ in their price flexibility and in their strategies for responding to dynamic electricity prices. Given such diversity, an implementer’s first inclination might be to start from scratch to define all these devices and to engineer their seemingly unique interactions. Given that each implementer’s perspective may be narrow within a transactive energy network, it is unlikely that uniquely engineered systems would interact well. This is where the transactive network template is applicable. The transactive network template is a metamodel that has been developed to guide implementers as they configure their own transactive agent within a network of such agents. The object-oriented design of the transactive network template provides basic code object types that may be used and extended by implementers to represent each of the assets in their circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule power usage, balance electric supply and demand, and coordinate the exchange of electricity with the other transactive agents. This report addresses the conceptual transactive network template design. Implementers are directed to more formal design documents and reference implementations. A Python™-based1 reference implementation of the transactive network template has been coded, and three implementations have been configured to represent a national laboratory and two university campuses. Version 2 of the transactive node template generalizes the market class and its methods to facilitate multiple, and more diverse market coordination mechanisms than were facilitated by and demonstrated using Version 1. Version 3 includes new Appendix B, which addresses the designs of methods that would make dynamic prices track approved electricity rates. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow. Development of the transactive network template is jointly funded by the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy and the DOE Office of Electricity. In late 2015, one of the first projects to be funded by the DOE Grid Laboratory Modernization Laboratory Consortium was the Clean Energy and Transactive Campus project, led by Pacific Northwest National Laboratory. DOE funds were matched by an investment by the Washington Department of Commerce through its Clean Energy Fund. The transactive network template was developed to guide the implementation of transactive energy networks within this project’s scope.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Applications of Hyporheic Zone Sampling for Assessing VOC Plume Engineered Remediation and Natural Attenuation Effectiveness - 20414

Determining the efficacy of engineered remedial actions and/or natural attenuation as an acceptable EPA Record of Decision (ROD) must address long-term temporal and spatial variability. Since final plume downslope destinations are aquifers and related surface streams and rivers, hyporheic zone sampling is essential. Comprehensive hyporheic sampling documents plume groundwater remediation prior to upwelling into stream benthic strata and natural attenuation within this dynamic zone itself. Our intensive sampling of the Savannah River Site (SRS) Chemical, Metals, and Pesticides (CMP) Pits VOC plume-fringe from 2005 to 2018 documented PCE natural attenuation and effectiveness of In Situ Thermal Treatment (ISTT). Most plume hyporheic upwelling was delimited to a 20 m inflow reach along Pen Branch. Our study utilized corroborative sampling methodologies: ambient hole-water grab samples: passive diffusion bags (PDB); and hole-core sediment samples to confirm station to station spatial and temporal trends. VOC hot spot detections were confirmed from both groundwater and soil core samples. VOC pathways through the Pen Branch hyporheic zone were assessed at hole-installation using sequential soil samples from augered cores and grab-samples of hole-bottom water. Longer term, composite water samples were collected from PDBs deployed for a minimum two-week equilibration period. Soil samples were collected from the 15 and 65 cm core levels in December 2017 and June 2018 at six stations. Additional core depths of 40 and 85 cm were sampled in June 2018 at the highest VOC station (5DB80). All samples were analyzed in an EPA certified lab by purge and trap GC-MS. Effectiveness of previous ISTT and soil vapor extraction (SVE) for this CMP Pits PCE plume, was clearly documented by significant downward trends in VOC at hyporheic zone stations prior to December 2015. Achieving final stage conversions of PCE daughter products to ethene and CO{sub 2} may be limited depending upon suitable interactions between hyporheic zone sediment morphology, hydrology, and redox conditions. Because declines in VOC daughter products at our hyporheic stations may also be caused by physical actions of dilution and dispersion rather than true biochemical dechlorination, full quantification of natural attenuation requires the use of compound-specific isotope ratio analysis (CSIA) and microbial 16S rRNA with qPCR. To assist these next-phase efforts, a natural attenuation index (NAI) was used to help produce quicker, more cost-effective decision making for locating CSIA and microbial sampling efforts. The NAI used natural logarithm transformed GC-MS results for station VOC degradation compounds versus VOC parent compounds. Wide differences were observed between stations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗

Williston Basin Resource Study for Commercial-Scale Subsurface Hydrogen Storage

The Energy & Environmental Research Center (EERC), in partnership with the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL), the EERC’s State Energy Research Center (SERC), MPLX Operations LLC, and TC Energy Development Holdings Inc. (a subsidiary of TC Energy Corporation), studied the potential for subsurface hydrogen storage and recovery in the Williston Basin of western North Dakota. The project’s goal was to evaluate the feasibility of large-scale, secure geologic H 2 storage to support future hydrogen generation, storage, and use. This work included laboratory testing, H 2 –rock–fluid exposure experiments, literature reviews on H 2 embrittlement, and reservoir modeling and simulations. The study included an assessment of storage potential across three types of storage reservoirs using both reservoir simulation and DOE’s web-based tool SHASTA-HELP (Subsurface Hydrogen Assessment, Storage, and Technology Acceleration – Hydrogen Estimator for Logistical Planning), as well as investigation of potential H 2 production and markets for commercial-scale deployment. Building on prior EERC gas storage research, three storage options were selected for detailed evaluation: the Broom Creek Formation (a clastic saline reservoir), the Dickinson Lodgepole Mounds (DLM) complex (carbonate mud mound structures) of the Lodgepole Formation (an active oil and gas producing reservoir), and the Dunham Salt Interval of the Piper Formation (to be used for engineered salt cavern development). These targets were prioritized based on prior EERC research using datasets related to seal capacity, reservoir quality, mechanical integrity, and injectivity. Exposure tests on Broom Creek and DLM samples showed mineral dissolution and precipitation that increased brine salinity and altered reservoir rock surfaces. Although these results provide useful insight, they are limited by small sample sizes and short-term (30-day) exposure, requiring further study to assess long-term storage integrity. Salt formations were not tested because of their known nonreactivity and established mechanical stability. Results of reservoir simulations performed for a single site demonstrated that the Broom Creek Formation may be capable of receiving up to 42,000 tonnes of injected H 2 over 7 months via one well. H 2 recovery took place over 5 months, resulting in approximately 26,000 tonnes (~62% without cushion gas [CG]). This work suggests water production may be important and subsequent cycles of injection and production may perform more efficiently; however, significant site-specific work in the future is needed to assess actual reservoir performance of injection and withdrawal of H 2 storage. For oil reservoir potential, a multiple-well model was used to simulate injection of approximately 32,000 tonnes of H 2 into a single wellbore while simultaneously producing in place reservoir fluids from four offset wells to maintain reservoir pressure. The simulation results suggested a high recovery (~98%); in addition, cost advantages through existing infrastructure could be realized. Challenges in this reservoir include vi managing gas purity and leakage risks. In both scenarios, production of H 2 takes place in a single-well scenario with 10 cycles (7 months of injection and 5 months of production) over 10 years. Finally, the use of engineered caverns in the Dunhan Salt was evaluated, and the results suggest that while they have a smaller capacity (<1000 tonnes per cavern), they exhibit nearly complete gas recovery (>99%), fast response times, and low purity risk. While caverns in North Dakota may be smaller in capacity, fields can be developed in galleries to accommodate the volumetric needs and rapid turnaround times necessary to meet market demands. Geographic limitations and thin salt intervals in North Dakota may represent less total storage potential than salt domes elsewhere, but significant opportunities exist to expand this market for gas storage in North Dakota. A basinwide assessment was performed to estimate a first-of-its-kind value for H 2 storage on a large scale. DOE’s SHASTA-HELP, combined with EERC simulation work, was used to perform the assessment. Estimated H 2 storage potential varied widely for each formation type. The Broom Creek saline formation was estimated to have a storage potential of approximately 1.7–90.5 million tonnes (MMt). The DLM oil reservoirs were estimated to have 0.07–0.19 MMt of capacity. Notably, each of these estimates relies on significant assumptions regarding reservoir thickness, porosity, permeability, and CG needed for operation. Much research is needed to understand the true site-specific storage resource potential of each formation. Using the Dunham Salt Interval for cavern development may result in as much as 4.79 MMt (up to 2.87 MMt working gas) of H 2 storage potential. An important note for consideration is that the values presented here need significantly more geological characterization and engineering assessments prior to gaining confidence in performance. This will be a focal point for future research and development needs. The basinwide evaluation also indicated that North Dakota has significant H 2 generation potential, with estimates up to a possible 13 MMt annually, suggesting a substantial opportunity for H 2 market development and thus the need for commercial-scale H 2 storage to facilitate growth and resilience. Key Recommendations 1. Conduct detailed site characterization (3D seismic, well logs, core sampling) to reduce geologic uncertainty. 2. Perform techno-economic analyses incorporating market, regulatory, and incentive frameworks. 3. Investigate long-term interactions among H 2 , CGs, well materials, and formations to assess risks. 4. Develop pilot- and field-scale demonstrations to validate models and establish best practices.

03 NATURAL GAS↗

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↗