Search Results (55)

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article

Infrared imaging technology has been widely adopted for industrial gas leak detection due to its capability for large field-of-view, long-range, and dynamic monitoring. However, in practical applications, natural object interference within the scene, together with the blurred contours and low contrast of infrared images, severely degrades the performance of gas detection and leakage region segmentation. To address these challenges, this paper proposes a gas leak detection method that integrates gas characteristics with spatiotemporal information. Specifically, the non-solid characteristics of gas are incorporated to constrain the foreground extraction process of the Gaussian Mixture Model (GMM), thereby suppressing interfering moving objects. Furthermore, by exploiting the spatiotemporal information in infrared image sequences, a multi-scale cross-attention fusion model is designed to fuse multi-scale and global feature representations, improving the accuracy of foreground detection. Finally, density-based clustering is employed to achieve complete segmentation of gas regions with irregular shapes. Experimental results demonstrate that the proposed method effectively suppresses interference from solid objects, accurately detects gas leakage, and successfully segments the diffusion regions. Compared with existing approaches, the proposed method shows significant advantages and provides a valuable reference for research on infrared imaging-based gas leak detection. Full article

Methane emissions from end-use installations in residential natural gas systems remain poorly quantified, despite their importance to both safety and climate policies worldwide. While distribution networks and appliances have received research attention, interior piping between the meter and appliances represents a critical knowledge gap. To address this gap, a systematic survey of 473 residential systems in Saarlouis, Germany, was conducted using standardized pressure decay tests (DVGW G 600). Measurements were performed during the installation of gas regulators necessitated by a grid pressure increase from 23 mbar to 55 mbar above ambient. This provided a unique opportunity to assess whole-system leakage under controlled conditions without installation modifications. Leak rates were standardized to reference pressure and converted to methane emissions using measured gas composition, using a linear pressure scaling as a provisional approximation valid for the small pressure differences in the applied test conditions. A total of 411 (86.9%) installations showed no detectable leak rate (LDL: 0.2 $\mathrm{L}{\mathrm{h}}^{-1}$). However, seven systems (1.5%) exceeded 1 $\mathrm{L}{\mathrm{h}}^{-1}$, and one surpassed the unacceptable threshold of 5 $\mathrm{L}{\mathrm{h}}^{-1}$. Mean emissions across all systems were 0.067 [0.041, 0.098] $\mathrm{g}{\mathrm{h}}^{-1}$, with smaller installations showing higher volume-normalized rates. Critically, fewer than 1.48% of systems contributed more than 46% of total emissions, demonstrating a strongly skewed, heavy-tailed distribution. Scaled nationally using Monte Carlo methods accounting for sampling uncertainty and skewed distributions, residential interior piping contributes 12.30 [8.11, 18.55] $\mathrm{G}\mathrm{g}{\mathrm{year}}^{-1}$ to Germany’s methane emissions. These results emphasize the need to include residential leak rates in emission inventories and highlight the efficiency potential of targeted mitigation strategies focused on high-emitting installations under evolving EU methane regulations. Full article

Methane (CH₄) emissions in urban areas remain a major source of uncertainty in greenhouse gas inventories, particularly in Eastern European cities, where observational studies are limited. This study presents a comprehensive, system-based assessment of CH₄ sources in Cluj-Napoca, Romania, based on high-resolution in situ measurements across five representative urban systems: aquatic environments (AQs), natural gas distribution end-use points (NG), sewer infrastructure (SE), building basements (BSs), and traffic emissions (TEs). Elevated CH₄ concentrations were consistently detected across all investigated systems, confirming the coexistence of both diffuse and point sources within the urban environment. Dissolved methane (dCH₄) in aquatic systems showed strong and persistent oversaturation relative to atmospheric equilibrium, reaching up to 3 × 10⁵% of air–water equilibrium, indicating active microbial methanogenesis enhanced by urban inputs of organic matter and nutrients. Measurements at natural gas end-use points revealed highly localized leaks with concentrations up to 482 ppmv. Sewer infrastructure exhibited extreme variability (up to 1222 ppmv), likely controlled by a combination of microbial production, hydraulic conditions, and potential interactions with adjacent gas distribution networks. Basement environments showed CH₄ accumulation up to 12 ppmv, reflecting the combined effects of gas leakage and limited ventilation. Measurements at vehicle exhausts identified transient CH₄ peaks reaching 162 ppmv during vehicle engine acceleration, with distinct ethane-to-methane ratios, indicative of pyrogenic sources. Overall, these results demonstrate that urban CH₄ emissions are spatially heterogeneous, temporally variable, and derived from multiple coexisting sources. The urban area should, therefore, be understood as a hybrid environment, with natural and anthropogenic CH₄ contributions. Full article

Hydrogen is a key energy carrier for fuel cell vehicles and hydrogen energy systems. However, its colorless and odorless nature, combined with a wide flammability range, poses significant safety risks in the event of leakage. Accordingly, compact and reliable hydrogen sensors capable of low-ppm detection at moderate operating temperatures are essential for early-stage safety monitoring. In this study, a bias-optimized hydrogen gas sensor based on a Pd-functionalized SnO₂ thin film with Mo electrodes and an integrated microheater is designed, fabricated, and systematically characterized. The sensor employs a Mo-based vertical microheater and a multilayer thermal insulation stack, enabling thermally efficient and stable operation at 250–280 °C with low power consumption. The electrical and sensing properties of the SnO₂ layer are optimized by controlling the oxygen partial pressure during reactive sputtering and post-deposition annealing. The Pd catalytic layer promotes hydrogen dissociation and spillover, resulting in pronounced resistance modulation through surface redox reactions and interfacial charge transport effects. By systematically optimizing the sensing bias voltage, a clear trade-off between sensitivity enhancement and electrical noise is identified, which allows stable and repeatable operation in the low-ppm regime. The sensor response follows a power-law dependence on hydrogen concentration, and an automated measurement platform is employed to evaluate repeatability and statistical performance. Based on baseline noise analysis and concentration-dependent resistance variation, a limit of detection of approximately 6.4 ppm is achieved. Furthermore, a concentration-normalized figure of merit that combines response magnitude and concentration dependence is introduced to quantitatively assess low-concentration hydrogen sensing performance. These results demonstrate that the proposed Mo-electrode Pd/SnO₂ thin-film sensor, enabled by bias-optimized operation and integrated thermal control, provides a robust and scalable platform for safety-critical hydrogen leak detection. Full article

Shale gas extraction is underway in the Karoo Basin. Previous oil and gas explorers abandoned several wells, and the abandonment statuses of these wells are unknown. Critically, improperly abandoned wells can provide a pathway for the leakage of stray gas into shallow aquifers and degrade water quality. To understand the abandonment integrity risk posed by these wells, a qualitative risk model was developed to assess the likelihood of well-barrier failure leading to a potential leak. The potential leak paths identified include zones with cement losses during grouting, casing corrosion, cement channels, failure to case and cement risk zones, uncased and uncemented sources, uncemented annuli, and unplugged wells. To confirm whether these wells are leaking, geochemical tracing of stray gas was integrated. Eleven of the fifty samples collected had dissolved hydrocarbon gas concentrations that were high enough to use isotopic analysis to determine the source. The results revealed microbial gas via fermentation and carbon dioxide reduction, thermogenic gas, and geothermal gas, as evidenced by larger δ¹³C₁ values and isotopic reversals associated with dolerite intrusions. The thermogenic-type gas detected in legacy abandoned wells and <1 km water boreholes adjacent to these wells serves as evidence that the downhole plugs did not maintain their integrity or were improperly plugged, whereas the thermogenic gas detected in >1 km water boreholes indicates leakage contamination due to natural fracture pathways. The presence of thermogenic gas in legacy wells and in groundwater boreholes <1 km from legacy wells implies that shale gas extraction using hydraulic fracturing cannot be supported in these situations. However, using safety buffer zones greater than 1 km from the legacy wells for shale gas drilling could be supported. Full article

Europe’s energy transition beyond 2025 faces a resilience gap as reconfigured pipeline flows, stricter methane rules, and rising variable renewables increase the need for seasonal flexibility and system adequacy. This study examines how Ukraine’s gas transmission network and underground gas storage—among the largest in Europe—can serve as a “seasonal battery” for the EU. We integrate a policy and market review with quantitative scenarios for 2026–2030. Methods include security-of-supply indicators (the rule that the system must keep operating even if its largest single infrastructure element fails, peak-day coverage, and winter adequacy), estimates of market-accessible storage volumes and withdrawal rates for European market participants, and a techno-economic screening of hydrogen-readiness comparing repurposing with new-build options. Methane intensity constraints and compliance with monitoring, reporting, and verification and leak detection and repair requirements are applied. The results indicate that reallocating part of Europe’s seasonal balancing to Ukrainian underground gas storage can enhance resilience to extreme winter demand and liquefied natural gas price shocks, reduce price volatility and the curtailment of variable renewables, and enable phased, cost-effective hydrogen corridors via repurposable pipelines and compressors. We outline a policy roadmap specifying transparent access rules, interoperable gas quality and methane standards, and risk mitigation instruments needed to operationalise cross-border storage and hydrogen-ready investments without carbon lock-in. Full article

Methane emissions at oil and gas facilities can be measured in real time with continuous monitoring systems that alert operators of upset conditions, including fugitive emissions. We report on extensive operator field testing of a continuous laser monitoring system in ~year-long deployments at 46 oil and gas sites in two U.S. basins. The operator assessed periods of non-alerts with daily optical gas imaging sweeps to confirm emission status. Detection precision was 98% and false positive and negative rates were 3%. Quantification of challenge-controlled release tests at active oil and gas sites yielded a measured versus true emissions curve with slope = 1.2, R² = 0.90. Repeatability test measurements of four production facilities with two different laser systems showed 33.9% average quantification agreement. Separate third-party blind controlled release testing at two state-of-the-art test facilities yielded 100% true positive rate (0 false negatives). Combining the third-party blind tests with field tests, emission rate quantification uncertainty was +/−41% across five orders of magnitude. These varied evaluation approaches validate the measurement system and operator integration of data for measurement and monitoring of upstream oil and gas emissions and demonstrate a test regime for vetting of monitoring and measurement technologies in active oil and gas operations. Full article

One of the biggest risks to safety on offshore platform safety is the ignition of high-pressure natural gas streams. Currently, the size and number of fugitive emissions on offshore platforms is unknown and methods used to detect fugitives have significant shortcomings. To investigate the frequency, size, and potential impact of fugitives, a data collection exercise was conducted using incidents reported, leak survey data, and independent measurements. The size and number of fugitives on offshore facilities were simulated to investigate likely areas of safety concern. Incident reports indicate in 2021 there were 113 reports of gas leaks on 1119 offshore facilities, suggesting 0.02 fugitives per Type 1 facility (older, shallow-water platforms) and 0.31 fugitives per Type 2 facility (larger deeper-water facilities). Leak survey data report 12 fugitives per Type 1 facility (average emission 0.6 kg CH₄ h⁻¹ leak⁻¹) and 15 fugitives per Type 2 facility (average emission 1.5 kg CH₄ h⁻¹ leak⁻¹). Reconciliation of direct measurements with a bottom-up model suggests that the number of fugitive emissions generated from the leak report data is an underestimate for Type 1 platforms (44 fugitives facility⁻¹; average emission 0.6 kg CH₄ h⁻¹ leak⁻¹) and in general agreement for the Type 2 platforms (15 fugitives facility⁻¹; average emission 1.5 kg CH₄ h⁻¹ leak⁻¹). Analysis of the fugitive emission rates on an offshore platform suggests that gas will not collect to explosive concentration if any air movement is present (>0.36 mph); however, large volumes of air (~600 m³) near representative leaks on the working deck could become explosive in hour-long zero-wind conditions. We suggest that wearable technology could be employed to indicate gas build up, safety regulations amended to consider low-wind conditions and real-world experiments are conducted to test assumptions of air mixing on the working deck. Full article

The growing demand for natural gas and the corresponding expansion of pipeline networks have intensified the need for precise leak detection, particularly due to the increased vulnerability of infrastructure to natural disasters such as earthquakes, floods, torrential rains, and landslides. This research leverages deep learning to develop two hybrid architectures, the Transformer–LSTM Parallel Network (TLPN) and the Transformer–LSTM Cascaded Network (TLCN), which are rigorously benchmarked against Transformer and Long Short-Term Memory (LSTM) baselines. Performance evaluations demonstrate TLPN achieves exceptional metrics, including 91.10% accuracy, an 86.35% F₁ score, and a 95.20% AUC value. Similarly, TLCN delivers robust results, achieving 90.95% accuracy, an 85.76% F₁ score, and 93.90% of the Area Under the ROC Curve (AUC). These outcomes confirm the superiority of attention mechanisms and highlight the enhanced capability realized by integrating LSTM with Transformer for time-series classification. The findings of this research significantly enhance the safety, reliability, sustainability, and risk mitigation capabilities of buried infrastructure. By enabling rapid leak detection and response, as well as preventing resource waste, these deep learning-based models offer substantial potential for building more sustainable and reliable urban energy systems. Full article

Well integrity is paramount for the safe, environmentally responsible, and economically viable operation of wells throughout their lifecycle, encompassing conventional oil and gas production, unconventional resource extraction (e.g., shale gas and tight oil), and geological storage applications (CO₂, H₂, and natural gas). This review presents a comprehensive synthesis of well integrity challenges, failure mechanisms, monitoring technologies, and management strategies across these operational domains. Key integrity threats—including cement sheath degradation (chemical attack, debonding, cracking, microannuli), casing failures (corrosion, collapse, burst, buckling, fatigue, wear, and connection damage), sustained casing pressure (SCP), and wellhead leaks—are examined in detail. Unique challenges posed by hydraulic fracturing in unconventional wells and emerging risks in CO₂ and hydrogen storage, such as corrosion, carbonation, embrittlement, hydrogen-induced cracking (HIC), and microbial degradation, are also highlighted. The review further explores the evolution of integrity standards (NORSOK, API, ISO), the implementation of Well Integrity Management Systems (WIMS), and the integration of advanced monitoring technologies such as fiber optics, logging tools, and real-time pressure sensing. Particular emphasis is placed on the role of digital technologies—including artificial intelligence, machine learning, and digital twin systems—in enabling predictive maintenance, early failure detection, and lifecycle risk management. The novelty of this review lies in its integrated, cross-domain perspective and its emphasis on digital twin applications for continuous, adaptive well integrity surveillance. It identifies critical knowledge gaps in modeling, materials qualification, and data integration—especially in the context of long-term CO₂ and H₂ storage—and advocates for a proactive, digitally enabled approach to lifecycle well integrity. Full article

An underground utility tunnel (UUT) is essential for the efficient use of urban underground space. However, current maintenance systems rely on patrol personnel and professional equipment. This study explores industrial detection methods for identifying and monitoring natural gas leaks in UUTs. Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. Based on a convolutional recurrent neural network (CRNN), the image features at each moment were extracted, and the time series data were modeled to realize the risk-level classification mechanism based on the automatic classification of the leakage rate. The experimental results show that when the sliding window size was set to 10 frames, the classification accuracy of the CRNN was the highest, which could reach 0.98. This method improves early warning precision and response efficiency, offering practical technical support for UUT maintenance management. Full article

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article

Carbon Capture and Storage (CCS) is a key technology for reducing anthropogenic greenhouse gas emissions, in which pipelines play a vital role in transporting CO₂ captured from industrial emitters to geological storage sites. To aid the efficient and safe operation of the CO₂ transport infrastructure, robust, accurate, and reliable solutions for monitoring pipelines transporting industrial CO₂ streams are urgently needed. This literature review study summarizes the monitoring objectives and identifies the problems and relevant mathematical algorithms developed for optimization of monitoring systems for pipeline transportation of water, oil, and natural gas, which can be relevant to the future CO₂ pipelines and pipeline networks for CCS. The impacts of the physical properties of CO₂ and complex designs and operation scenarios of CO₂ transport on the pipeline monitoring systems design are discussed. It is shown that the most relevant to liquid- and dense-phase CO₂ transport are the sensor placement optimization methods developed in the context of detecting leaks and flow anomalies for water distribution systems and pipelines transporting oil and petroleum liquids. The monitoring solutions relevant to flow assurance and monitoring impurities in CO₂ pipelines are also identified. Optimizing the CO₂ pipeline monitoring systems against several objectives, including the accuracy of measurements, the number and type of sensors, and the safety and environmental risks, is discussed. Full article

To address the challenges of insufficient model generalization, high false alarm rates due to the scarcity of leakage data, and frequent minor leakage alarms in traditional weak leakage (the leakage amount is less than 1%) detection methods for gas transmission pipelines, this paper proposes a real-time weak leakage detection framework for natural gas pipelines based on the combination of the generalized likelihood ratio (GLR) and ensemble learning. Compared to traditional methods, the core innovations of this study include the following: (1) For the first time, GLR statistics are integrated with an ensemble learning strategy to construct a dynamic detection model for pipeline operating states through multi-sensor collaboration, significantly enhancing the model’s robustness in noisy environments by fusing pressure data from the pipeline inlet and outlet, as well as outlet flow data. (2) An adaptive threshold selection mechanism that dynamically optimizes alarm thresholds using the distribution characteristics of GLR statistics is designed, overcoming the sensitivity limitations of traditional fixed thresholds in complex operating conditions. (3) An ensemble decision module is developed based on a voting strategy, effectively reducing the high false alarm rates associated with single models. The model’s leakage detection capability under normal and noisy pipeline conditions was validated using a self-built gas pipeline leakage test platform. The results show that the proposed method can achieve the precise detection of pipeline leakage rates as small as 0.5% under normal and low-noise conditions while reducing the false alarm rate to zero. It can also detect leakage rates of 1.5% under strong noise interference. These findings validate its practical value in complex industrial scenarios. This study provides a high-sensitivity, low-false-alarm, intelligent solution for pipeline safety monitoring, which is particularly suitable for early warning of weak leaks in long-distance pipelines. Full article