EOR Technology (Patents) and Science (Articles) Assessment of BRICS and nonBRICS with Growth Rates and Specializations within Responsible Global Energy Transition: A Critical Review
Bibliographic record
Abstract
To achieve a low-carbon energy transition, it is essential to ensure that, as long as fossil fuels are needed, their production is sustainable, minimizing the environmental impact and securing resources for advancing greener technologies, in alignment with SDGs 7, 13, and 14. Enhanced oil recovery (EOR) increases the recovery rates without new developments. The recent expansion of the BRICS consortium, involving major producers, underscores the need to evaluate their EOR technologies, particularly potential gaps that could hinder global energy transition strategies. We analyzed intermediate levels of technological readiness levels (TRLs) utilizing patents (TRL4-5) and articles (TRL3) for 18 EOR methods between 2002 and 2021. Composite indicators derived from patents including compound annual growth rate, specialization, concentration, diversification, and Gini inequality were employed. Both BRICS and nonBRICS exhibited analogous distributions in the articles (particularly Norway, United Kingdom, Canada) and patents (particularly Russia, China, and Ukraine). The decline in growth rates among BRICS and negative rates in nonBRICS suggest a technological plateau for traditional methods. However, environmentally low-impact EOR methods are experiencing exponential emergence (low salinity water, MEOR, polymers and macromolecular compounds, their associations with surfactants, and WAG). Both groups are self-sufficient in EOR, ensuring a responsible and low-impact energy transition. This ensures energy quality while facilitating the maturation of renewable technologies.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".