A mini-investigation on enhanced oil recovery evolution (2007 – 2020)
Bibliographic record
Abstract
Energy plays an important role in sustaining humanity. With rising worldwide energy demand and the great dependence of energy generation on fossil fuels, it is inevitable that enhanced oil recovery must be deployed to recover more possible reserves. This report focuses on reviewing publications related to enhanced oil recovery from 2007 to 2020 through the utilization of bibliometric analysis. Of the 5498 documents retrieved from Web of Science, 569 journals, 90 countries, 2025 organizations, and 8684 authors are involved. China, the United States, Iran, Canada, and India published the most documents. The United States has the highest h-index at 61. The analysis of keywords had shown that the hot issues lie around four main domains namely carbon capture, utilization, and sequestration (CCUS), microbial enhanced oil recovery (MEOR), development of unconventional reserves, and chemical enhanced oil recovery. This study provides some useful insights for future research directions. From there, discussions were subsequently placed on chemical EOR.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".