Bibliographic record
Abstract
Abstract This paper summarizes the results of reservoir simulations performed to investigate steam-air injection and its potential as a thermal recovery process. The steam-air injection process is considered to be any process that involves both steam and air injection whether they are injected together or at different times. The objective of the simulations was to develop steam-air injection based on its ability to divert steam from more to less oil depleted regions thereby improving reservoir contact (conformance) and increasing oil production. This diversion is caused by the deposition of coke and/or asphaltene in depleted regions or high permeability channels or thief zones. It primarily results from low temperature oxidation (LTO) of the oil. Air injection is most effective when used to divert injected steam when significant channeling/over-riding is occurring prior to air injection. Air should be injected at a relatively low rate and temperature in order that coke/asphaltene deposition occurs and facilitates steam diversion. Simulations showed that in a field application of the steam-air injection process, it is important to stop injecting air with/without steam once diversion has been obtained. Otherwise, oil will unnecessarily be consumed or degraded by LTO reactions and steam diversion may be reduced due to consumption of deposited coke/asphaltenes by high temperature oxidation (HTO) reactions. Continued injection of air could even increase channeling as it removes the coke and any residual oil by oxidation reactions thereby reducing the flow resistance in channels/thief zones. Air injection in a steam process is complex and it is not simply a case of injecting air with steam without careful consideration of the process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".