Evaluation of Different Factors on Enhanced Oil Recovery of Heavy Oil Using Different Alkali Solutions
Bibliographic record
Abstract
A series of sand pack flood tests are carried out on Court heavy oil using different alkali solutions to evaluate the influence of different factors on enhanced oil recovery of heavy oil, such as interfacial tension (IFT), emulsification effect, and pressure drop. These alkali solutions include NaOH, Na 2 CO 3, NaOH–surfactant, Na 2 CO 3 –surfactant, and the mixtures of NaOH and Na 2 CO 3 at different ratios. The results demonstrated that using NaOH solution to displace heavy oil obtained the best oil recovery efficiency but without the lowest IFT and the most effective emulsification. By correlation of the enhanced oil recovery efficiencies with IFTs, emulsification effects, and pressure drops, it was found that the oil recovery efficiency corresponded better with the increments in pressure drop than other factors after chemical slug injection. In combination with the discovery of micromodel tests, it was deduced that the improvement on the heavy oil recovery efficiency was mainly due to the formation of an oil bank, which plugged the water channel. The formation of the oil bank for the NaOH displacing process is due to the accumulation of oil droplets. While for the NaOH–surfactant flooding process, the formation of the oil bank is mainly because of the emulsification. OH – exerts a special influence on the separation of trapped oil into oil droplets and the accumulation of oil droplets. A certain amount of OH – is required to reduce the IFT, which is beneficial to the formation of oil droplets, while excessive OH – can promote the accumulation of oil droplets, which is also detrimental to the formation of oil droplets.
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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".