Infrared thermography monitoring of LNG receiving stations has the characteristics of large size of key facilities and complex site conditions, which put forward higher requirements on data cleaning, identification and location of cold leakage defects of infrared thermography. Using infrared thermal imaging technology to monitor key facilities of LNG receiving stations can characterize the correspondence between equipment operation status and surface temperature, and at the same time convey the current operation information or fault situation of key facilities of LNG receiving stations, which is important for early leakage monitoring and early warning of LNG site facilities. This paper proposed an intelligent monitoring method for leakage defects of key facilities in LNG receiving stations, which integrated data cleaning, leakage defects monitoring and intelligent identification, in order to address the problems that easily occur in the application of infrared thermal imaging monitoring technology in liquefied natural gas (LNG) receiving stations. Firstly, a cleaning method of infrared thermal imaging monitoring data based on the combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) was established, which can accurately identify the video frames of foreign objects intruding into the field of view of the lens and mark them as abnormal, reduce the interference of abnormal objects to the monitoring process, and reduce the cleaning accuracy. The cleaning accuracy rate is over 95%. Further, for the problems that the abnormal data of key facilities in LNG receiving stations are very few, which leads to misjudgment and untimely abnormality identification, and the infrared monitoring is easily affected by the surrounding environment, an abnormality monitoring method based on convolutional neural network was proposed. After comparison and analysis, the method proposed in this paper can remove the limitation of boundary setting, effectively identify the scenes where personnel enter the monitoring screen to different degrees, and identify the abnormality of another facility in the same category more accurately by learning the abnormality of a facility in the same category. That is the convolutional neural network can well identify the case of one insulation defect by learning the normal scene and the scene containing two insulation defects in advance. The storage tank is selected as the research object, and a specific convolutional neural network is constructed to identify the abnormal moments of the storage tank by training the historical data and then. The advantage is that it has good learning among different individuals of the same kind of facilities and the recognition accuracy is up to 99%.

Long-distance oil and gas pipeline is a key component of oil and gas strategic import channel. It have great significant to ensure the efficient and stable operation of pipeline for energy security. In order to meet the development needs of the integrity of long-distance pipelines, the development process, research hotspots, development status and existing bottlenecks in China were analyzed in this paper. The development plan was proposed from two levels of key technology and scientific research support conditions. As to the technical level, the full pipeline life cycle coverage, high-precision defect detection, risk management and control, emergency intelligent decision-making, and comprehensive application of core technologies such as information security assurance should be achieved. In another the localization of core equipment types, and the independent research and development system and key equipment should be fully applied in oil and gas pipelines. The localization of core equipment, fully apply self-developed systems and key equipment on oil and gas pipelines at home and abroad should be improved. The support in policies should be provided such as pipeline integrity management system, institutional mechanism construction and safety legislation. At the same time, the investment in stable scientific research in personnel cultivation and laboratory construction, strengthen the promotion and application of results, and strengthen scientific and technological exchanges and cooperation to better ensure the development and improvement of pipeline integrity technology should be enhanced.

With the scientific and technological goals of “Three Deeps Leading”, China’s oil and gas resource exploration is advancing from shallow strata to deep strata, and the construction of kilometer level deep underground engineering has become the norm. With the exploration of deep ground engineering, available deep underground spaces such as depleted oil and gas reservoirs, salt caverns, and goafs have emerged. Deep underground spaces have been proven to have advantages in large-scale energy storage and utilization, such as large reserves, wide distribution, high safety, low economic costs, and environmental friendliness. The industrial energy storage application of deep underground spaces is a powerful means to optimize China’s energy storage structure and ensure the national energy storage strategy needs. However, China started relatively late in the research of safety and emergency guarantee technology for energy storage safety in deep underground spaces. Currently, commonly used surface oil and gas storage methods are limited by geographical environment, construction conditions, and transportation methods. At the same time, accidents in surface oil and gas storage facilities face challenges such as complex causal factors, strong correlation, and wide disaster coverage. Therefore, the safe storage of energy in deep underground spaces is urgent. At present, China’s utilization of deep underground space energy storage focuses on the redevelopment of depleted oil and gas reservoirs, and the existing deep ground safety protection technology lags behind the practical needs of deep ground energy storage infrastructure safety protection. Therefore, this article systematically summarized the current research status of deep ground energy storage technologies such as deep gas storage, hydrogen storage, carbon sequestration, and compressed air energy storage. The goal was to achieve green transformation in the energy industry with strategic energy security storage. It emphasized the integration of energy storage, carbon sequestration, and deep resource extraction, and proposes a dual carbon cycle architecture for deep space energy storage and utilization. At the same time, the risks faced by deep ground energy storage equipment and facilities were systematically identified, covering the entire life cycle of deep ground energy storage design, construction, operation, and abandonment. The development needs, difficulties, and suggestions for China’s deep ground energy storage safety and emergency support technology were revealed, and a technical framework for the full life cycle safety and emergency support of deep ground energy storage was constructed. The development suggestions for China’s deep underground space safety and emergency support technology from 2024 to 2050 were proposed, providing reference for improving China’s deep underground space energy storage safety and emergency support technology system.

The oil and gas station is an important part of the transportation and storage system of oil and natural gas in China. The safe and stable operation of the oil and gas station is an important part of the oil and gas system and even the national economic development. China has a vast territory with diverse climate types, including storm, lightning, typhoon and other meteorological disasters. When meteorological disaster occurs in and around oil and gas stations and yards, it may cause damage to relevant installations, leading to abnormal operation of stations and yards, and even accidents. The medium in the oil and gas stations usually has significant characteristics of flammability and explosion. In the common situations, petroleum and related liquid processing products are generally volatility and easy to form flammable gas clouds. Natural gas stations are often accompanied by high pressure operation conditions. Once abnormal working condition or accident occurs, the life safety of staffinside the station area and the surrounding environment will bear risks. That’s difficult to avoid totally meteorological disasters, but targeted safety measures can be taken to strengthen the stability of stations to resist meteorological disasters and reduce the loss. Here, considering the equipment of oil and gas station often has more oil and gas leakage, explosion and other accidents when encountering meteorological disasters such as rainstorm and thunderstorm, this paper proposed a quantitative vulnerable node identification method according to the occurrence scenario of meteorological disasters and the possible disaster evolution path in related scenarios. The methods often adopted at present lack fineness and accuracy in identifying vulnerable nodes of station equipment, which may cost a lot to protect the wrong nodes and vulnerable nodes, but still lead to leakage of storage tank pipelines, or even high risk of explosion. Based on the improvement of gravity model and the Leuven algorithm, this paper proposed a method to identify vulnerable nodes of equipment in community analysis model. This paper revealed five vulnerable nodes, including automatic fire extinguishing system, ranking highest in rainstorm, and five vulnerable nodes, including lightning rod, ranking highest in thunderstorm. Compared with other analysis methods, the precision of community analysis model increased by more than 10%, the accuracy of analysis results increased by more than 10%, and the recall rate increased by more than 14%. The fragile nodes in historical cases can be identified, and more vulnerable nodes can be identified.

In recent years, harsh environmental conditions (lightning, wind and rain) have posed significant threats to the safe operation of long oil and gas pipelines, especially in oil and gas pipelines in other countries. The operation and emergency responses for oil and gas pipelines abroad have faced various problems such as insufficient pipeline risk accident data and difficult cross-border coordination during harsh environmental conditions. Current pipeline accident early warning models rely too much on field data or accident-related data. Thus, this paper presents a station accident risk evolution early warning method based on a knowledge graph that uses a small amount of accident report data from natural gas pipeline stations for harsh environmental conditions. The method uses a bidirectional long short-term memory-conditional random field algorithm (Bi-LSTM-CRF) to extract the causal relationships from station accident reports, with a Neo4j graph database then used to establish the knowledge graph for accident risk evolution in natural gas pipeline stations abroad experiencing harsh environmental conditions. The results show that this knowledge graph based station accident risk early warning method not only provides earlier warnings of station accidents than traditional station accident early warning methods for long pipelines, but also predicts the accident path with recommended accident responses. The results show that this early warning method can effectively help safety management personnel in natural gas pipeline stations abroad provide better risk control and accident prevention.