An Analysis of the Intellectual Property Market in the Field of Enhanced Oil Recovery Methods
Bibliographic record
Abstract
The article presents an analysis of the intellectual property market in the field of enhanced oil recovery (EOR) methods. The search retrospectively covers the period from 2010 to 2020. Russia, China, and the United States are the leading countries in enhanced oil recovery methods. Canada, Germany, and Saudi Arabia also have a high level of patent activity compared with other countries. Semantic and statistical analysis of the obtained sample of documents made it possible to highlight the areas of intensive patenting, high competitiveness, as well as mainstream methods of enhanced oil recovery. The analysis of the leading companies’ patent portfolios revealed the similarities and differences in their structure. Tatneft, ConocoPhillips Co., Sinopec, and PetroChina Co. are actively patenting in the field of thermal enhanced oil recovery, which has been identified as the mainstream. BASF SE is focused on the production of chemicals, including chemicals for oil production. The Saudi Arabian Oil Company produces light oil using waterflooding and physicochemical methods. Software dominates the patent collection sector in the EORs of Gazpromneft STC and Lukoil.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".