A Bibliometric Analysis on the Safety of Oil and Gas Pipelines: Research Trends and Perspectives

Abstract

Oil and gas pipeline safety analyses aim to enhance the integrity management and operational safety of pipelines to prevent major incidents. This study seeks to provide a comprehensive overview of the evolution of research hotspots and knowledge frontiers in global oil and gas pipeline safety. A bibliometric data mining method was adopted to systematically sort out 2,582 studies in the field of oil and gas pipeline safety retrieved from the core database of Web of Science from 2000 to 2023. A bibliometric network analysis was conducted to investigate the distribution of papers in terms of time, geographic region, organization, main author, journal cocitation, and literature cocitation, and to identify the research hotspots and frontiers. The findings revealed that the number of papers on oil and gas pipeline safety has surged from 10 in 2000 to 327 in 2023, reflecting an overall exponential growth trend. Key research contributions in this field predominantly originate from institutions such as the China University of Petroleum (Beijing), Southwest Petroleum University, and China University of Petroleum (East China). These institutions, located primarily in China, have had a significant impact on the field. Notable researchers, including Li Yuxing, Faisal Khan, and Zhang Hong, have made substantial contributions. Leading journals, such as Journal of Loss Prevention in the Process Industries, Process Safety and Environmental Protection, and Energies, have published the highest number of influential papers in this area. The research indicates that although a foundational theory and research framework for oil and gas pipeline safety have been established, numerous research directions and cutting-edge branches continue to emerge. Prominent among these are studies focusing on pipeline reliability analysis, risk assessment, failure prediction, leakage, and multiphase flow assurance, using numerical simulations and machine learning techniques. This study provides a theoretical basis and practical guidance for advancing oil and gas pipeline safety analysis and accident prevention.