Recovery Factors in High-Pressure Air Injection Projects Revisited
Bibliographic record
Abstract
Abstract High-Pressure Air Injection (HPAI) is an 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. (SPE 28733) proposed a method for estimating recovery factors of light-oil air-injection projects based on the performance of two successful HPAI projects. Their suggested method relies on the extrapolation of the field gas-oil ratio 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 a HPAI process is mostly due to re-pressurization 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 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 predicted by the gasflood approach.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".