Inline Monitoring of Hydrogen Blends and Automated Pipe Leakage Detection
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03 NATURAL GAS↗
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Time-Lapse Gravity Monitoring of CO2 Migration Based on Numerical Modeling of a Faulted Storage Complex
In this study, the performance of both surface and borehole time-lapse gravity monitoring to detect CO2 leakage from a carbon storage site is evaluated. Several hypothetical scenarios of CO2 migration in a leaky fault, and thief zones at different depths at the Kimberlina site (California, USA) constitute the basis of the approach. The CO2 displacement is simulated using the TOUGH2 simulator applied to a detailed geological model of the site. The gravity responses to these CO2 plumes are simulated using forward modeling with sensors at ground surface and in vertical boreholes. Results of inversion on one scenario are also presented. The surface-based gravity responses obtained for the different leakage scenarios demonstrate that leakage can be detected at the surface in all the scenarios but the time to detection is highly variable (10 to 40 years) and dependent on the detection threshold considered. Borehole measurements of the vertical component of gravity provide excellent constrains in depth when they are located in proximity of the density anomaly associated with the presence of CO2, thus discriminating multiple leaks in different thief zones. Joint inversion of surface and borehole data can bring valuable information of the occurrence of leakages and their importance by providing a reasonable estimate of mass of displaced fluids. This study demonstrates the importance of combining multiphase flow simulations with gravity modeling in order to define if and when gravity monitoring would be applicable at a given storage site.
Time-lapse gravity monitoring, leak detection, CCS↗
Role of Histidine‐Containing Peptoids in Accelerating the Kinetics of Calcite Growth
Carbonate mineralization, the conversion of CO 2 into stable, thermodynamically favorable carbonate minerals, offers a promising strategy for permanent and environmentally friendly carbon storage, with minimal risk of long-term leakage and minimal monitoring requirements. Drawing inspiration from carbonic anhydrase (CA), a family of zinc-containing metalloenzymes that catalyze the hydration of CO 2 to bicarbonate and promote carbonate precipitation, a class of histidine-containing peptoids was designed that is capable of coordinating with Zn 2+ ions to act as CA mimetics for accelerating calcite step growth. In situ atomic force microscopy (AFM) measurements reveal that these peptoids significantly enhance step advancement, with a more pronounced effect observed when combined with Zn 2+ ions and under higher calcium-to-carbonate activity ratios, indicating that peptoids facilitate the incorporation of CO 3 2− ions at step edges. Solution NMR and 3D atomic force microscopy (3D AFM) analyses show that the coordination of peptoids with Zn 2+ promotes both the deprotonation of HCO 3 − to CO 3 2− and restructures the interfacial hydration layers of calcite, collectively lowering the activation barrier for step growth. These findings establish a design framework for sequence-defined polymers to regulate carbonate mineralization, offering promising applications in CO 2 capture and long-term storage.
CO2 mineralization↗
Challenges and Opportunities for Basic Efficiency Measures in Low-Income Homes: A Southeast Alaska Case Study
Juneau, Alaska is the state's capital city and has a renewable energy goal to reach 80% renewable energy for the space heating and transportation sectors by 2045. In practical terms, this indicates a need to electrify both sectors, to take advantage of the inexpensive hydropower available from the local electric utility, Alaska Energy Light & Power. In an effort to complement and enable electrification, researchers examined the feasibility to deploy storm windows via a case study of installing storm windows in two local low-income homes. Newer models of storm windows provide an extra layer of insulation over existing windows, while preserving operability and views. They can also improve comfort and reduce noise. Researchers worked with the regional housing authority and a local builder to install the storm windows and replace inoperable windows in the two houses in 2021, in addition to conducting pre- and post-installation air leakage tests, energy monitoring, and occupant interviews. The team encountered several challenges, including a lack of egress windows, energy data from a wide variety of heating systems, extremely leaky homes, and installation issues such as windows that were not square. These results point to several barriers to widespread deployment of storm windows in the area, but also open the door to opportunities to design deployment programs that improve safety as well as efficiency.
cold climate↗
Challenges and Opportunities for Basic Efficiency Measures in Low-Income Homes: A Southeast Alaska Case Study
Juneau, Alaska, is the state's capital city and aims to reach 80% renewable energy for the space heating and transportation sectors by 2045. This goal highlights a need to electrify both sectors to take advantage of the inexpensive hydropower available from the local electric utility, Alaska Electric Light and Power. To that end, researchers examined the feasibility of deploying storm windows via a case study of installing storm windows in two local low-income homes. Newer models of storm windows provide an extra layer of insulation over existing windows while preserving operability and views. They can also improve comfort and reduce noise. In addition to conducting pre- and post-installation air leakage tests, energy monitoring, and occupant interviews, researchers worked with the regional housing authority and a local builder to install the storm windows and replace inoperable windows in the two houses in 2021. The team encountered several challenges, including a lack of egress windows, energy data from a wide variety of heating systems, extremely leaky homes, and installation issues, such as windows that were not square. These results point to several barriers to the widespread deployment of window upgrades in the area and open the door to opportunities to design deployment programs that improve safety and efficiency.
cold climate↗
RAMM-TM for detection of gas leakage from canisters containing spent nuclear fuel
Remote Area Modular Monitoring (RAMM) for canister surface temperature measurement (TM), or RAMM-TM, is a novel remote monitoring device for detection of gas leakage based on surface temperature measurements of canisters containing spent nuclear fuel. Here we describe the development of RAMM-TM, as is the demonstration of its performance in detecting canister gas leakage from a small, simulated chloride-induced stress corrosion crack in experiments using a 1/4.5-scale model cask. Both helium and air gas leakage from a canister were detected within hours after the start of the leakage. The change in surface temperatures at the top and bottom of the canister (ΔTBT) during gas leakage (depressurization) triggered automatic alarms, providing a sound basis for early detection of gas leakage from the canister. This methodology would allow consequence management through the implementation of mitigatory actions to continue effective aging management and to reduce risks to public safety, health, and the environment.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks
Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.
Bayesian network, Risk assessment, Monitoring, car↗
Node Monitoring as a Fault Detection Countermeasure against Information Leakage within a RISC-V Microprocessor
Advanced, superscalar microprocessors (μP) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the μP. Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The μP designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the μPs.
42 ENGINEERING↗
Economic assessment of seismic monitoring for underground hydrogen storage
Underground hydrogen storage (UHS) plays a key role in the energy landscape. However, like other subsurface engineering technologies, UHS may cause leakage into the groundwater or atmosphere and possibly induce local seismicity. To reduce these risks, seismic monitoring could be a viable technique to track the UHS plume, detect leakages, and locate induced seismicity events. Seismic monitoring has been proposed to safely monitor UHS, but research in this area is still new and requires field studies. Lab and theoretical studies have demonstrated the validity of seismic monitoring for UHS. Therefore, it is imperative to analyze the economic feasibility of seismic monitoring for UHS. Hence, we develop a cost model and open-source Python code for seismic monitoring that considers types of seismometers, comprehensive operational scenarios, detection thresholds, and long-term leakage monitoring. A case study is further provided to validate the cost model on reservoir simulations of UHS. We find that the levelized cost for a 10-year operating UHS site will range on the order of ∼0.003 $\$$/kg. The methods developed in this study could also be applied to the monitoring of groundwater, gas, and/or wastewater injection.
08 HYDROGEN↗
Quantum Fourier Transform with Leakage Control
In circuit QED (cQED) systems, one can manipulate the cavity Fock states to use quantum processing unit (QPU). Since the coherence times of the Fock states are long, qudit based computation can be possible. One of the most basic gate that can be engineered in this cQED platform is the displacement gate, $D(\alpha)$. However, accessible number of qudit-levels is finite. The truncated displacement gate on the qudit is the source of theoretical error above the logical states and needs to be addressed. In this work, we monitor and minimize the leakage in qudit systems that involves displacement gate by introducing so-called `bumper' states and a modified cost function.
Kurkcuoglu, D. [Fermilab] (ORCID:0000000311097074)↗
In-situ proton radiation testing of 2.4 micron wavelength extended InGaAs photodiodes at dry ice and room temperatures
We have successfully tested 290 µm diameter, 2.4 micron wavelength, Extended InGaAs photodiodes coupled with single mode fiber using 50 MeV Protons at both dry ice temperature (-72 °C) and room temperature (20 °C). The devices were reverse biased at 100 mV during the radiation run and their leakage current was continuously monitored insitu during the exposure. These devices find multiple applications in space for spectroscopy & sensing, inter-satellite optical communication links, rapid Doppler shift LIDAR, as well as inter-planetary and Earth-to-Moon communication links. Several photodiodes were tested using 50 MeV Protons with an average flux level of 2.11 × 10 7 protons/cm 2 /s, for a total fluence of 1.0 × 10 11 protons/cm 2 and total dose of 20 krad (water). Pre- and post-radiation results were also measured for leakage current vs. voltage, responsivity (quantum efficiency), and bandwidth of the Extended InGaAs photodiodes. Finally, all devices were found to be fully functional at normal operating conditions and at both dry ice and room temperature.
2.4 micron wavelength↗
Time-lapse seismic data inversion for estimating reservoir parameters using deep learning
Geologic carbon sequestration involves the injection of captured carbon dioxide ([Formula: see text]) into subsurface formations for long-term storage. The movement and fate of the injected [Formula: see text] plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for [Formula: see text] saturation monitoring to determine the geologic controls on the storage of the injected [Formula: see text]. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular [Formula: see text] injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in [Formula: see text] saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the [Formula: see text] saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of [Formula: see text] saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.
Geochemistry & Geophysics↗
Identification of potential CO 2 leakage pathways and mechanisms in oil reservoirs using fault tree analysis
Geological storage of CO 2 technologies has become an important and effective way to reduce the greenhouse gas emissions, especially when it is combined with CO 2 enhanced oil recovery (EOR), which can not only trap CO 2 but also enhance oil recovery. However, the risk of CO 2 leakage has always been a prominent issue. In this paper, the mechanisms and pathways of CO 2 leakage during geological storage in oil reservoirs were analyzed using fault tree analysis (FTR). Besides, monitoring technologies were discussed and deployed in a CO 2 EOR demonstration project. The analysis results showed that the sealing failures of oil producer and CO2 injector wells, like well cement failure and casing failure, are the main reasons for the CO 2 leakage, which has been observed in the oil field monitoring project. The monitoring results indicated that there is no large-scale CO 2 leakage, while relatively high and abnormal CO 2 concentration in soil gas near some wellbores are observed, which indicates there is some leakage of CO 2 through incomplete cement ring and well casing string. FTR results provide guidelines for monitoring and preventing of CO 2 leakage during geological storage in oil reservoirs. Finally, the near-surface monitoring methods, especially the soil gas monitoring technologies, can effectively detect the leakage of CO 2 , and are a proper method for CO 2 leakage monitoring.
58 GEOSCIENCES↗
Robotics for HVAC applications: A critical review and future perspectives
Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.
AI↗
Safety issues of defective lithium-ion batteries: identification and risk evaluation
Lithium-ion batteries inevitably suffer minor damage or defects caused by external mechanical abusive loading, e.g. , penetration, deformation, and scratch without triggering a hard/major short circuit. The replacement of cells becomes a dilemma if the safety risk of the defective batteries remains unknown. Herein, by introducing a representative defect form, i.e. , screw indentation, we demonstrate the safety characteristics of defective batteries. We prove that defective batteries have a significantly increased thermal risk and deteriorated mechanical integrity, but can go undetected due to prompt voltage recovery and insignificant local temperature increase. We discover that the voltage curve within the first few cycles contains sufficient information to identify defective batteries from otherwise good ones and propose methodologies to monitor the cells. Capacity loss and current leakage are two characteristics that can be estimated using the voltage curve. According to the defect size and position, the capacity loss could be 1 to 10 2 mA h and the leakage current could be 5–50 mA. Results remove the barriers for defective battery safety risk evaluation, enabling identification, monitoring, and early warning of minor damaged batteries.
25 ENERGY STORAGE↗
Legacy Well Leakage Risk Analysis at the Farnsworth Unit Site
This paper summarizes the results of the risk analysis and characterization of the CO 2 and brine leakage potential of Farnsworth Unit (FWU) site wells. The study is part of the U.S. DOE’s National Risk Assessment Partnership (NRAP) program, which aims to quantitatively evaluate long-term environmental risks under conditions of significant geologic uncertainty and variability. To achieve this, NRAP utilizes risk assessment and computational tools specifically designed to quantify uncertainties and calculate the risk associated with geologic carbon dioxide (CO 2 ) sequestration. For this study, we have developed a workflow that utilizes physics-based reservoir simulation results as input to perform leakage calculations using NRAP Tools, specifically NRAP-IAM-CS and RROM-Gen. These tools enable us to conduct leakage risk analysis based on ECLIPSE reservoir simulation results and to characterize wellbore leakage at the Farnsworth Unit Site. We analyze the risk of leakage from both individual wells and the entire field under various wellbore integrity distribution scenarios. The results of the risk analysis for the leakage potential of FWU wells indicate that, when compared to the total amount of CO 2 injected, the highest cemented well integrity distribution scenario (FutureGen high flow rate) exhibits approximately 0.01% cumulative CO 2 leakage for a 25-year CO 2 injection duration at the end of a 50-year post-injection monitoring period. In contrast, the highest possible leakage scenario (open well) shows approximately 0.1% cumulative CO 2 leakage over the same time frame.
54 ENVIRONMENTAL SCIENCES↗
Mechanistic modeling of CO 2 well leakage in a generic abandoned well through a bridge plug cement-casing gap
Both known and unmapped plugged and abandoned wells are potential leakage pathways for CO 2 from geologic carbon sequestration (GCS) sites. Although many abandoned wells have cement bridge plugs installed to prevent leakage, the seal between the cement and the inner casing wall is subject to failure. In this study, we carried out detailed T2Well simulations of cases of sudden non-Darcy flow of CO 2 and brine leakage up the gap between a cement plug and the inner steel casing wall that becomes a fully connected flow path during the post-injection period. The goal of our study was two-fold: (1) to understand the dynamics, rates, and the characteristic temporal signals associated with the onset of leakage through various gap-aperture sizes, and (2) to suggest potential monitoring strategies based on the findings. Simulation results show that the leakage of CO 2 and brine upward is transient with interesting phase interference behavior. Time-dependent oscillatory flows with varying pressure, temperature, and flow rates of CO 2 and brine show strong dependence on gap aperture. Phase-change and decompression lead to very low temperatures at the top of the well for gap apertures larger than 4 mm suggesting that remote thermal monitoring at the ground surface may be an effective way of monitoring even if well locations are not known a priori. Pressure in the well is also indicative of CO 2 leakage. The temporal patterns of changing temperature and pressure may be useful diagnostic signals for leakage detection. Finally, these transient leakage signals may provide information on the cause of leakage and/or characteristics of the flow path that could inform effective remediation design and execution approaches.
58 GEOSCIENCES↗
An adaptable technique for comparative image assessment: Application to crosswell electromagnetic survey design for fluid monitoring
Reservoir integrity stewardship accompanying carbon capture and sequestration considers fluid extraction and reinjection as a risk-mitigating method against overpressuring that could lead to caprock damage and ensuing [Formula: see text] leakage. Crosswell electromagnetics offers a technically viable monitoring method with the spatial volume coverage necessary for reservoir-encompassing pressure management. However, a certain logistic dilemma for deep gas sequestration into saline and thus electrically conductive aquifers is that crosswell magnetic-field measurements underperform in the imaging of more resistive plume bodies, further exacerbated when vertical arrays intersect, as opposed to surround, plumes. Furthermore, comparative synthetic-data plume imaging of such scenarios rates the information content of magnetic-field versus electric-field 3D crosswell layouts for reservoir and infrastructure conditions of a representative pilot site in a coastal area in Florida. The image quality of the resulting plume replications can be ranked numerically through a newly proposed semblance qualifier, appraising the model goodness of fit to a given reference. In contrast to common least-squares measures for goodness of fit, the semblance formulation uses classifying logistic function types, thus enabling a better distinction of predefined anomaly features.
58 GEOSCIENCES↗