Experimental study of enhanced oil recovery by CO2 huff-n-puff in shales and tight sandstones with fractures
Bibliographic record
Abstract
Abstract The fractures and kerogen, which generally exist in the shale, are significant to the CO2 huff-n-puff in the shale reservoir. It is important to study the effects of fractures and kerogen on oil recovery during CO2 huff-n-puff operations in the fracture–matrix system. In this study, a modified CO2 huff-n-puff experiment method is developed to estimate the recovery factors and the CO2 injectivity in the fractured organic-rich shales and tight sandstones. The effects of rock properties, injection pressure, and injection time on the recovery factors and CO2 usage efficiency in shales and sandstones are discussed, respectively. The results show that although the CO2 injectivity in the shale is higher than that in the sandstone with the same porosity; besides, the recovery factors of two shale samples are much lower than that of two sandstone samples. This demonstrates that compared with the tight sandstone, more cycles are needed for the shale to reach a higher recovery factor. Furthermore, there are optimal injection pressures (close to the minimum miscible pressure) and CO2 injection volumes for CO2 huff-n-puff in the shale. Since the optimal CO2 injection volume in the shale is higher than that in the sandstone, more injection time is needed to enhance the oil recovery in the shale. There is a reference sense for CO2 huff-n-puff in the fractured shale oil reservoir for enhanced oil recovery (EOR) purposes.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".