Heavy-Oil Recovery by Combined Hot Water and Alkali/Cosolvent/Polymer Flooding
Bibliographic record
Abstract
Summary A hybrid process is developed and optimized for heavy-oil recovery that combines moderate reservoir heating and chemical enhanced oil recovery in the form of alkali/cosolvent/polymer flood. The process is simulated by use of a model derived from existing laboratory and pilot data of a 5,000-cp heavy-oil field. It is found that hot waterflooding is efficient in heating the reservoir only when high early injectivity is achievable. This may not be the case if incipient fluid injectivity is low and/or long, continuous, horizontal shale baffles are present. To remedy the former, an electrical-preheating period is devised, whereas switching to a horizontal flood could overcome the latter. Once the reservoir temperature is raised sufficiently, a moderately unstable alkali/cosolvent/polymer flood is capable of mobilizing and displacing oil. A best combined strategy for efficient reservoir heating, high oil recovery, and cost effectiveness is found to involve reducing the oil viscosity to values of approximately 300–500 cp and combining a degree of mobility control and low interfacial tension as recovery mechanisms.
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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".