Abstract:

Safety management of long-distance oil and gas pipeline enterprises has been regarded as essential for ensuring energy security and maintaining social stability. As a key approach to enhancing safety management efficiency, audits of QHSE management systems are increasingly emphasized. In this study, traditional audit modes—including international safety rating audits, full-factor quantitative audits, and system certification audits—were systematically compared and analyzed. Additionally, the features and applicable contexts of emerging audit modes, such as data-driven audits and AI-based audits, were further explored. It was found that each audit mode presented distinct advantages and limitations, depending on the enterprise’s stage of development, risk management demands, and resource capabilities. Accordingly, a comprehensive audit framework integrating conventional methods and intelligent technologies was constructed. Optimization strategies were proposed, including phased audit mode selection, risk-oriented focus, capacity enhancement, digital and intelligent transformation, and the establishment of a closed-loop rectification mechanism. The findings show that a multi-mode integrated audit system significantly improves the safety performance of long-distance oil and gas pipeline enterprises. The conclusions provide theoretical guidance and practical reference for the selection and optimization of QHSE audit modes in the oil and gas industry.

Key words: long-distance oil and gas pipelines, quality, health, safety and environment (QHSE) management system, audit modes, data-driven, artificial intelligence (AI) audit