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Gas leaks in the oil and gas industry represent a safety risk as they, if ignited, may result in severe fires and/or explosions. Unignited, they have environmental impacts. This is particularly the case for methane leaks due to a significant Global Warming Potential (GWP). Since gas leak rates may span several orders of magnitude, that is, from leaks associated with potential major accidents to fugitive emissions on the order of 10−6 kg/s, it has been difficult to organize the leaks in an all-inclusive leak categorization model. The motivation for the present study was to develop a simple logarithmic table based on an existing consequence matrix for safety related incidents extended to include non-safety related fugitive emissions. An evaluation sheet was also developed as a guide for immediate risk evaluations when new leaks are identified. The leak rate table and evaluation guide were tested in the field at five land-based oil and gas facilities during Optical Gas Inspection (OGI) campaigns. It is demonstrated how the suggested concept can be used for presenting and analysing detected leaks to assist in Leak Detection and Repair (LDAR) programs. The novel categorization table was proven valuable in prioritizing repair of “super-emitter” components rather than the numerous minor fugitive emissions detected by OGI cameras, which contribute little to the accumulated emissions. The study was limited to five land based oil and gas facilities in Norway. However, as the results regarding leak rate distribution and “super-emitter” contributions mirror studies from other regions, the methodology should be generally applicable. To emphasize environmental impact, it is suggested to include leaking gas GWP in future research on the categorization model, that is, not base prioritization solely on leak rates. Research on OGI campaign frequency is recommended since frequent coarse campaigns may give an improved cost benefit ratio.

Optical Gas Imaging (OGI) cameras represent an interesting tool for identifying leaking components in hydrocarbon processing and transport systems. They make it possible to see exactly where a leak originates, thereby enabling efficient leak detection and repair (LDAR) programs. The present paper reports on an OGI test campaign initiated by the Norwegian Environmental Agency (NEA), and how this campaign stimulated cross-disciplinary cooperation at an LNG plant for better control of both fugitive hydrocarbon emissions and safety-related leaks. A surprising potentially severe leak detected in the NEA campaign triggered the introduction of in-house OGI cameras at plants and refineries, and an inter-disciplinary cooperation between specialists in the environment, technical safety and operations. Some benefits of in-house OGI cameras, as well as some concerns regarding their use are presented and discussed. The general experience is that an Ex safe, i.e., rated for safe use in a combustible hydrocarbon gas atmosphere, OGI camera, represents a very valuable tool for detecting fugitive emissions as the start point for LDAR programs. An OGI camera did, however, also turn out to be a valuable tool for fire and explosion risk management, and has led to reduced downtime after leak incidents. The concerns relate to leaks seen through the OGI camera that may look overwhelming, even with concentrations well below the ignitable limits of the released gas. Based on the LNG plant experiences, it is generally recommended that specialists in the environment, technical safety, operations and teaching fields cooperate regarding the introduction and use of OGI cameras. Suggestions for training courses are also discussed.

The hydrocarbon leaks from process systems potentially lead to hazardous consequences with regard to human safety, environmental pollution and valuable assets. The hydrocarbon leaks may be gas leaks, liquid leaks or multiphase leaks. The gas leaks have the highest potential of damage due to explosion accidents. both gas and oil leaks can create long-lasting fires threatening personnel safety and structural integrity of process plants and offshore platforms. One common method for limiting the consequences associated with a process emergency is the rapid depressurization or blowdown of pressurized process systems. There is experimental evidence that the assumption of thermodynamic equilibrium is not appropriate during rapid depressurization, since the two phases show an independent temperature evo- lution. The current work proposes a model for the simulation of the blowdown of vessels containing two-phase (gas–liquid) hydrocarbon fluids, considering partial phase equilibrium between phases. Two phases may be present either already at the beginning of the blowdown process (for instance in gas– liquid separators) or as the liquid is formed from flashing of the vapour due to the cooling induced by pressure decrease. In addition, the transient behaviour of hydrocarbon leaks from pressurized process systems during depressurization is also included in the model providing the inputs for risk assessments. The model is based on a compositional approach, and it takes into account coupled effects of internal heat and mass transfer processes, as well as heat transfer with the vessel wall and the external environ- ment. The vapour liquid equilibria calculations are performed using dynamic link library provided by the comprehensive pressure volume temperature and physical properties package ‘Multiflash’. Numeri- cal simulations show a generally good agreement with experimental measurements.

Leakage of flammable gases and the resulting explosions in petrochemical plants remain latent risks, capable of occurring at any moment. Therefore, to address these worst-case scenarios within a virtual reality framework, we conducted simulations aimed at predicting and effectively responding to potential damages due to gas leakage. This study presents an analysis of the hazards that can lead to leaks and potential explosions in a petrochemical plant using the BREEZE Incident Analyst program. Rapid and accurate recognition of the risk associated with gas leaks, which can cause extensive damage and explosions, is of paramount importance. This study addresses two main aspects: the prediction of the consequences of gas leaks through simulations and the implementation of appropriate detection measures. Better, more efficient risk management and mitigation strategies were implemented by predicting gas leak paths using BREEZE. Using ultrasonic detection technology, detection was demonstrated to be possible in approximately one-third the time required by conventional detectors, and it is weather-insensitive. Simultaneously, considering plant characteristics such as utility configurations, we propose an additional method to prevent leaks from going undetected. This is achieved by integrating gas detection technology that combines both existing and new technologies.

Previous research has shown that natural gas (NG) leaks from residential appliances are common, affecting greenhouse gas emission inventories and indoor air quality. To study these implications, we collected and analyzed 587 unburned NG samples from 481 residences over 17 North American cities for hydrocarbons, hazardous air pollutants, and organosulfur odorants. Nearly all (97% of) gas samples contained benzene (between-city mean: 2335 ppbv [95% CI: 2104, 2607]) with substantial variability between cities. Vancouver, Los Angeles, Calgary, and Denver had at least 2x higher mean benzene concentrations than other cities sampled, with Vancouver exhibiting a nearly 50x greater mean benzene level than the lowest-concentration city (Boston). We estimate that current U.S. and Canadian emissions inventories are missing an additional 25 000 [95% CI: 19 000, 34 000] and 4000 [95% CI: 3700, 5200] lbs benzene yr −1 through downstream NG leakage, respectively. Concentrations of odorants added for leak detection varied substantially across cities, indicating a lack of standardization. Houston, for instance, had 5x higher mean tert-butyl mercaptan levels than Toronto. Using these odorant measurements, we found that methane emissions as high as 0.0080–0.28 g h −1 and indoor benzene enhancements 0.0096–0.11 ppbv could go undetected by persons with an average sense of smell, with large uncertainties driven by smelling sensitivity, gas composition, and household conditions. We also observed larger leaks (>10 ppm ambient methane) in ∼4% of surveyed homes, confirming that indoor leakage occurs at varying degrees despite the presence of odorants. Overall, our results illustrate the importance of downstream NG composition to understand potential emissions, exposures, and odor-mediated leak detection levels. Given methane’s global warming potency, benzene’s toxicity, and wide variation in smelling abilities, our findings highlight the deficiencies regarding the sole reliance on odorization to alert and protect all occupants from indoor leaks.

Gas leaks threaten ecological and social safety. Non-contact infrared imaging enables large-scale, real-time measurements; however, in complex environments, weak signals from small leaks can hinder reliable detection. This study proposes a novel automated methane leak detection method based on infrared imaging and a Gas-Faster Region-based convolutional neural network (Gas R-CNN) to classify leakage amounts (≥30 mL/min). An uncooled infrared imaging system was employed to capture gas leak images containing leak volume features. We developed the Gas R-CNN model for gas leakage detection. This model introduces a multiscale feature network to improve leak feature extraction and enhancement, and it incorporates region-of-interest alignment to address the mismatch caused by double quantization. Feature extraction was enhanced by integrating ResNet50 with an efficient channel attention mechanism. Image enhancement techniques were applied to expand the dataset diversity. Leak detection capabilities were validated using the IOD-Video dataset, while the constructed gas dataset enabled the first quantitative leak assessment. The experimental results demonstrated that the model can accurately detect the leakage area and classify leakage amounts, enabling the quantitative analysis of infrared images. The proposed method achieved average precisions of 0.9599, 0.9647, and 0.9833 for leak rates of 30, 100, and 300 mL/min, respectively.

The fate of deepwater releases of gas and oil mixtures is initially determined by solubility and volatility of individual hydrocarbon species; these attributes determine partitioning between air and water. Quantifying this partitioning is necessary to constrain simulations of gas and oil transport, to predict marine bioavailability of different fractions of the gas‐oil mixture, and to develop a comprehensive picture of the fate of leaked hydrocarbons in the marine environment. Analysis of airborne atmospheric data shows massive amounts (∼258,000 kg/day) of hydrocarbons evaporating promptly from the Deepwater Horizon spill; these data collected during two research flights constrain air‐water partitioning, thus bioavailability and fate, of the leaked fluid. This analysis quantifies the fraction of surfacing hydrocarbons that dissolves in the water column (∼33% by mass), the fraction that does not dissolve, and the fraction that evaporates promptly after surfacing (∼14% by mass). We do not quantify the leaked fraction lacking a surface expression; therefore, calculation of atmospheric mass fluxes provides a lower limit to the total hydrocarbon leak rate of 32,600 to 47,700 barrels of fluid per day, depending on reservoir fluid composition information. This study demonstrates a new approach for rapid‐response airborne assessment of future oil spills. Key Points Atmospheric hydrocarbon data define air‐water partitioning of marine oil spills Air‐water partitioning determines oil fate and extent in the marine environment These data permit a unique and robust calculation of oil leak rate

The results of field, analytical, and experimental research at a number of production facilities reflect the properties of oil-contaminated soils in 3 landscapes: the permafrost treeless Arctic ecosystem, boreal forest, and temperate-climate grassland-woodland ecotone. Laboratory studies have revealed the concentrations of petroleum hydrocarbons in soils, ranging from medium levels of 2000–3000 mg/kg to critical figures over 5000 mg/kg, being 2–25 times higher than the permissible content of oil products in soils. The experimentally applied thermal effects for the oil products desorption from the soil allowed finding an optimal regime: the treatment temperature from 25 to 250 °C reduces the concentrations to an acceptable value. The conditions are environmentally sound, given that the complete combustion point of humates is ca. 450 °C. The outcomes suggest the eco-friendly solution for soil remediation, preserving the soil fertility in fragile cold environments and in more resilient temperate climates, where revitalized brownfields are essential for food production.

Leaks from accidents or damage to pipelines that transport liquid petroleum hydrocarbons (PHC) such as gasoline and diesel are harmful to the environment as well as to human health, and may be hard to detect by inspection mechanisms alone when they occur in small volumes or persistently. In the present study, we aim to identify spectral anomalies in two plant species (Brachiaria brizantha and Neonotonia wightii) linked to contamination effects at different developmental phases of these plants. To do so, we used spectroscopy and remote sensing approaches to detect small gasoline and diesel leaks by observing the damage caused to the vegetation that covers simulated pipelines. We performed a contamination test before and after planting using gasoline and diesel volumes that varied between 2 and 16 L/m3 soil, in two experimental designs: (i) single contamination before planting, and (ii) periodic contaminations after planting and during plant growth. We collected the reflectance spectra from 35 to approximately 100 days after planting. We then compared the absorption features positioned from the visible spectral range to the shortwave infrared and the spectral parameters in the red edge range of the contaminated plants to the healthy plants, thus confirming the visual and biochemical changes verified in the contaminated plants. Despite the complexity in the indirect identification of soil contamination by PHCs, since it involves different stages of plant development, the results were promising and can be used as a reference for methods of indirect detection from UAVs (Unmanned Aerial Vehicles), airplanes, and satellites equipped with hyperspectral sensors.

In this paper, we describe both applied and analytical work in collaboration with a large multistate gas utility. The project addressed a major operational resource allocation challenge that is typical to the industry. We study the resource allocation problem in which some of the tasks are scheduled and known in advance, and some are unpredictable and have to be addressed as they appear. The utility has maintenance crews that perform both standard jobs (each must be done before a specified deadline) as well as respond to emergency gas leaks (that occur randomly throughout the day and could disrupt the schedule and lead to significant overtime). The goal is to perform all the standard jobs by their respective deadlines, to address all emergency jobs in a timely manner, and to minimize maintenance crew overtime. We employ a novel decomposition approach that solves the problem in two phases. The first is a job scheduling phase, where standard jobs are scheduled over a time horizon. The second is a crew assignment phase, which solves a stochastic mixed integer program to assign jobs to maintenance crews under a stochastic number of future emergencies. For the first phase, we propose a heuristic based on the rounding of a linear programming relaxation formulation and prove an analytical worst-case performance guarantee. For the second phase, we propose an algorithm for assigning crews that is motivated by the structure of an optimal solution. We used our models and heuristics to develop a decision support tool that is being piloted in one of the utility's sites. Using the utility's data, we project that the tool will result in a 55% reduction in overtime hours. This paper was accepted by Noah Gans, special issue on business analytics .