Sweep Efficiency Improvement by Alkaline Flooding for Pelican Lake Heavy Oil
Bibliographic record
Abstract
Abstract In this paper a laboratory study was reported for investigating a method to improve sweep efficiency by applying alkaline flooding for Pelican Lake reservoir. This included interfacial tension measurements, micromodel observatios and channelled sandpack flood tests. In total, 48 flood tests were conducted in channelled sandpacks to evaluate the chemical formulas and injection strategies for Pelican Lake oil. The first 14 sandpack flood tests were carried out to assess the potential of the alkaline flooding for the oil. The results suggested that 0.4 wt% NaOH and 0.2 wt% Na2CO3 was the optimum combination to maximize the oil recovery efficiency in the channelled sandpacks. Based on industry interest in using a NaOH-only slug injection, in the second step 34 flood tests were conducted with the injection of only NaOH solution. For Pelican oil, 0.6 wt% NaOH was the optimum concentration to maximize the oil recovery efficiency (~15% IOIP recovery) in NaOH-only injection. The sandpack flood results obtained in this study showed that formation of water-in-oil dispersion and improvement of sweep efficiency in channeled sandpacks did occur in the tertiary recovery process through the injection of NaOH-only solution.
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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 | 0.000 | 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".