Gas reservoir-type underground gas storage (UGS) plays a critical role in China’s natural gas reserves and peak shaving, serving as an essential component of the energy security system. Its unique cyclic injection and production operations not only stabilize the natural gas supply but also impose stringent requirements on the safety and integrity of geological structures, wellbores, and surface facilities. Weaknesses in current practices can cause accidents, directly threatening energy security. Therefore, continuously improving integrity management is the key to mitigating energy risks. Currently, the integrity management of gas storage faces challenges such as an abundance of standards and the complexity of management elements, which affect both operational safety and management efficiency. To address these issues, this study systematically analyzes domestic and international standards related to gas storage and establishes a technical system based on “three-in-one” integrity management (geological structure, wellbore, and surface facilities). Key elements of integrity management are identified and optimized, and recommended execution standards for critical factors are proposed to provide a theoretical basis and decision-making support for the safe operation of gas storage. This study not only offers a reference for optimizing and implementing integrity management standards but also has significant practical implications for enhancing energy security and reducing energy risks, ensuring the smooth execution of China’s natural gas reserve and peak shaving initiatives.

This paper presents the PALI team's winning system for SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation. We fine-tune XLM-RoBERTa model to solve the task of word in context disambiguation, i.e., to determine whether the target word in the two contexts contains the same meaning or not. In the implementation, we first specifically design an input tag to emphasize the target word in the contexts. Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not. The new vector is attained by concatenating the embedding of the [CLS] token and the embeddings of the target word in the contexts. In training, we explore several tricks, such as the Ranger optimizer, data augmentation, and adversarial training, to improve the model prediction. Consequently, we attain first place in all four cross-lingual tasks.

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks.