ANALYSIS OF THE INTERNATIONAL PRACTICE OF CO2 INJECTION AS ONE OF THE EOR METHODS
Bibliographic record
Abstract
CO 2 flooding is currently a popular method employed for enhanced oil recovery (EOR). The initial mention of utilizing CO 2 as an injection gas to enhance oil production dates back to 1916, although its economic feasibility was not established until the 1950s. Initially, alternative gases such as propane, liquefied petroleum gas, and natural gas were utilized, but CO 2 emerged as a more cost-effective and efficient option. The first CO 2 flooding project commenced in 1964, followed by a larger-scale project in 1972. Subsequently, successful CO 2 flood implementations have been witnessed in diverse regions including the United States, Canada, Hungary, Turkey, Trinidad, and Brazil. Among these, the United States stands out with 67 operational CO 2 flood projects, while other countries face challenges such as limited infrastructure and sources. To date, over 100 EOR projects involving CO 2 flooding have been recorded. This publication presents a comprehensive overview of international experiences and methodologies pertaining to the application of CO 2 flooding for enhancing oil production efficiency. Furthermore, it emphasizes the potential projects and applications of this technology within the context of Kazakhstan.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".