Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.142 | 0.159 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".