Recovery Factors in High-Pressure Air Injection Projects Revisited
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Bibliographic record
Abstract
Summary High-pressure air injection (HPAI) is an improved-oil-recovery (IOR) process in which compressed air is injected into a deep light-oil reservoir with the expectation that the oxygen in the injected air will react with a fraction of the reservoir oil at an elevated temperature to produce carbon dioxide. The resulting flue-gas mixture provides the main mobilizing force to the oil downstream of the reaction region, sweeping it to production wells. The combustion zone itself may provide a critical part of the sweep mechanism. In 1994, Fassihi et al. proposed a method for estimating recovery factors of light-oil air-injection projects on the basis of the performance of two successful HPAI projects. Their suggested method relies on the extrapolation of the field gas/oil ratio (GOR) up to an economic limit. In other words, it treats HPAI as an immiscible gasflood and neglects any potential oil that could be recovered by the combustion front. The truth is that, although early production during an HPAI process is caused mostly by repressurization and gasflood effects, once a pore volume of air has been injected, the combustion front becomes the main driving mechanism. Moreover, one of the unique features of air injection is the self-correcting nature of the combustion zone, which promotes good volumetric sweep of the reservoir. This paper presents laboratory and field evidence of the presence of a thermal front during HPAI operations and evidence of its beneficial impact on oil recovery. An analysis of the three HPAI projects in Buffalo field, which are the oldest HPAI projects currently in operation, shows that only a small fraction of the reservoir has been burned and, if time allows and the projects are managed appropriately, burning of more reservoir volumes could result in much higher oil recoveries than those predicted by the gasflood approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it