Investigating the <scp> CO <sub>2</sub> </scp> injection and the performance of nanoparticles in preventing formation damage at different pressures and concentrations
Bibliographic record
Abstract
Abstract Asphaltene deposition is one of the main challenges during CO 2 injection (miscible or immiscible) into reservoirs. Asphaltene deposition reduces the porosity and permeability of reservoir rock and changes its wettability to strongly oil‐wet. In this article, to control the asphaltene deposition during CO 2 injection the use of direct asphaltene inhibitors (Al 2 O 3 and Fe 3 O 4 ) in reservoir conditions. In this research, nanoparticle screening (to investigate its significance in reducing asphaltene precipitation) has been done and it can be stated that Al 2 O 3 nanoparticle will perform better effectivity than Fe 3 O 4 nanoparticle. After that, by using effective nanoparticles (Al 2 O 3 ) in different concentrations (1000 and 2000 ppm), the amount of asphaltene deposition during CO 2 injection has been investigated. The obtained results show that increasing the injection pressure (from immiscible to miscible) causes an increase in asphaltene deposition and the use of nanoparticles will reduce the amount of asphaltene deposition.
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".