Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 15, Issue 3
Abstract
The problem of corrosion in oil and gas pipelines is one of the major factors affecting the process safety and efficient sustainability development of the oil and gas industry. To gain a better understanding of global research trends and dynamics in the field of oil and gas pipeline corrosion and to advance the development of corrosion control technology, we conducted a literature review using a sample of 1,745 papers from the Web of Science (WOS) database published from 2002 to 2022. We employed a bibliometric analysis approach employed to investigate the distribution of publications over time, geographic regions, major organizations, major authors, journal cocitation, and literature cocitation, and to identify research hotspots and frontiers. The results revealed an exponential growth in the overall number of papers, with the most rapid increase occurring in the last 4 years. China, the US, Canada, the United Kingdom, and Brazil emerged as the most active countries in oil and natural gas pipeline corrosion research, and Mexico, Canada, and Australia also exhibited significant influence in the field. The journals Engineering Failure Analysis, Corrosion, and Corrosion Science had the highest number of publications and impact in this domain. Notably, Corrosion Science stood out as the most influential and highly regarded journal in the corrosion field. The fundamental theories and research framework in the realm of oil and natural gas pipeline corrosion have been primarily established, and a large number of research directions and frontier branches are emerging. The impact of flow parameters on corrosion, pipeline reliability assessment, and analysis of corrosion defects and failures are identified as the three main development paths in this field. In terms of research methodologies, machine learning techniques are becoming increasingly prevalent, with a growing number of studies adopting various machine learning methods. Among these methods, explainable deep learning is at the forefront of development in the field of oil and natural gas pipeline corrosion.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was funded by the Postdoctoral Research Program of PetroChina Southwest Oil & Gasfield Company (20220305-18).
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 15 • Issue 3 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Oct 13, 2023
Accepted: Dec 12, 2023
Published online: Mar 22, 2024
Published in print: Aug 1, 2024
Discussion open until: Aug 22, 2024
ASCE Technical Topics:
- Analysis (by type)
- Computer vision and image processing
- Corrosion
- Deterioration
- Energy engineering
- Energy infrastructure
- Energy sources (by type)
- Engineering fundamentals
- Failure analysis
- Fuels
- Gas pipelines
- Infrastructure
- Lifeline systems
- Materials characterization
- Materials engineering
- Methodology (by type)
- Natural gas
- Non-renewable energy
- Oil pipelines
- Petroleum
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