Global Trends in Heavy Oil and Bitumen Recovery and In-Situ Upgrading: A Bibliometric Analysis During 1900–2020 and Future Outlook
Bibliographic record
Abstract
Abstract Bitumen and heavy oil are energy resources with high viscosities, high densities, and high metals and heteroatoms content. This paper reports a bibliometric survey to investigate the historic trends and the future pattern of heavy oil and bitumen recovery and upgrading worldwide. It evaluates research outputs and their impact on the topic from 1900 to 2020. Data were extracted from Web of Science (WoS), vetted using Microsoft Excel, and visualized using VOSViewer. Globally, the study identified 8248 publications. Canada had the highest research output and was also widely cited, and the highest-productive countries are the United States from 1900 to 1970, Canada from 1971 to 2000, Canada from 2001 to 2010, and China from 2011 to 2020. The keywords frequency suggests that most research on heavy oil and bitumen focuses more on viscosity reduction, rheology, asphaltenes, enhanced oil recovery methods, and upgrading. These are the top five most productive institutions in the field: University of Calgary > China University of Petroleum > University of Alberta > Russian Academy of Sciences > China National Petroleum Corporation. The Universities of Calgary and Alberta are, however, the most frequently cited and most impactful, with respective citations and h-indexes of 10367 (50 h-index) and 8556 (47h-index). The future of heavy oil and bitumen depends on crude oil price, the economics of transportation alternatives, climate change policies and technologies, while the design of robust and low-cost catalysts would guide in-situ catalytic upgrading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.058 | 0.123 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".