Status Quo of a CO2-Assisted Steam-Flooding Pilot Test in China
Bibliographic record
Abstract
It is challenging to enhance heavy oil recovery in the late stages of steam flooding. This challenge is due to reduced residual oil saturation, high steam-oil ratio, and lower profitability. A field test of the CO2-assisted steam flooding technique was carried out in the steam-flooded heavy oil reservoir in the J6 block of the Xinjiang oil field (China). In the field test, a positive response to the CO2-assisted steam flooding treatment was observed, including a gradually increasing heavy oil production, an increase in the formation pressure, and a decrease in the water cut. The production wells in the test area mainly exhibited four types of production dynamics, and some of the production wells exhibited production dynamics that were completely different from those during steam flooding. After being flooded via CO2-assisted steam flooding, these wells exhibited a gravity drainage pattern without steam channeling issues, and hence, they yielded stable oil production. In addition, emulsified oil and CO2 foam were produced from the production well, which agreed well with the results of laboratory-scale tests. The reservoir-simulation-based prediction for the test reservoir shows that the CO2-assisted steam flooding technique can reduce the steam-oil ratio from 12 m3 (CWE)/t to 6 m3 (CWE)/t and can yield a final recovery factor of 70%.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".