ANALYSIS OF THE CARBON DIOXIDE ENHANCED OIL RECOVERY TECHNOLOGY
Bibliographic record
Abstract
The paper reposts on a comprehensive study of Carbon Dioxide Enhanced Oil Recovery (CO2-EOR), a detailed literature and projects review. In one hand, according to past studies, when injected CO2 and residual oil are miscible (Miscible Displacement), the physical forces holding the two phases apart (Interfacial Tension, IFT) disappears; as CO2 dissolves in the oil, it swells the oil, reducing its viscosity and density. This allows the oil CO2 to displace the oil from the rock pores, pushing it towards a production well. On the other hand, when injected CO2 and residual oil are not miscible (Immiscible Displacement), this process is used as a secondary recovery method. As many experts look to carbon capture, utilization and storage (CCUS) as one of the best alternatives for dealing with carbon emissions, research studies and laboratory investigations have indicated that, beyond its potential to augment oil production, CO2-EOR is getting intensive scrutiny by the industry, government, and environmental organizations for its potential for permanently storing CO2. A good example is a study by Montana Tech University, which found that CO2 flooding of Montana�s Elm Coulee and Cedar Creek oil fields could result in the recovery of 666 million barrels of incremental oil and the storage of 640 billion cubic meters of CO2, which is equivalent to 7 years of supplier�s CO2 emissions (a coal-fired power plant). Some other projects in the U.S., Canada and Norway have been evaluated. An economic and ecological analysis of the CO2-EOR process have been provided.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".