Enhanced Oil Recovery with Air Injection: Effect of the Temperature Variation with Time
Bibliographic record
Abstract
Gas-based enhanced oil recovery (EOR) processes rely on the injection of gases, such as carbon dioxide, nitrogen, and natural gas, into heavy oil reservoirs to reduce native oil viscosity. Although these processes are very promising, they face the problem of limited and costly gas supply. This paper investigates the conditions, specifically of temperature variation, under which freely available air at low temperatures and low pressures and in a non-reactive environment may be used for EOR. To that end, experiments are carried out by injecting air into a lab-scale heavy oil reservoir at different pressures (0.169, 0.286, 0.403, and 0.514 MPa absolute) and temperatures in the range of 25–90 °C. Reservoirs of four different permeabilities (40, 87, 204, and 427 darcy) are used in experiments, which demonstrate heavy oil recovery of up to 58.2% original oil in place (OOIP) with constant temperature air injection. When air is injected with a periodic temperature variation between 75 and 90 °C that has an average of 78 °C, the recovery is found to increase to 69.1% OOIP. This is an improvement of 18.6% over that using constant temperature air injection at the maximum temperature of 90 °C.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".