Characterization of Produced and Residual Oils in the CO<sub>2</sub> Flooding Process
Bibliographic record
Abstract
In immiscible CO 2 flooding processes, the injected CO 2 extracts light and intermediate components from reservoir hydrocarbons, changing the properties of both the produced and residual oils. CO 2 injection also increases asphaltene precipitation, which further causes adverse effects on the oil effective permeability, operational facilities, and the oil recovery. In this study, six CO 2 flooding followed by blowdown processes are carried out in the laboratory under four CO 2 injection pressures and two temperatures, and the properties of produced and residual oils are further characterized. It is found that uneven residual oil distribution occurred due to the coupled effects of viscous fingering and oil properties changes during CO 2 flooding process. Characterization of the produced oil shows that both the produced oil density and viscosity decrease when CO 2 injection pressure increases. The asphaltenes content of the oil produced after CO 2 breakthrough is found to be relatively lower than that of the oil samples collected before CO 2 breakthrough. In addition, the produced oil becomes lighter as the CO 2 injected volume increases. The saturates content in the residual oil decrease considerably in comparison with original crude oil in the CO 2 miscible flooding process. Furthermore, this study discovers that asphaltene component in produced oils is more stable than that in residual oils based on asphaltene-to-resin ratio (ATR) analysis. Finally, incremental oil recoveries of 2.78%–11.72% can be experimentally achieved through blowdown processes that are carried out after a 30 min soaking period. The laboratory study not only shows that blowdown processes can be successfully applied to improve the performance of CO 2 flooding processes, but also provides a deep understanding of properties changes of produced and residual oils in CO 2 -enhanced oil recovery processes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".