A Guide to High Pressure Air Injection (HPAI) Based Oil Recovery
Bibliographic record
Abstract
Abstract In recent years, high pressure air injection (HPAI) has proven to be a valuable IOR process, especially in deep, high pressure, low permeability fields where other recovery processes are uneconomic. This paper will provide engineers and engineering managers with a wide-ranging look at the key factors that should be addressed when considering a high pressure air injection (HPAI) based IOR process in a light oil reservoir. The paper is based on many years of involvement of the authors both in the laboratory and the field, as well as drawing on published literature. The main focus is on key design and operating criteria that must be considered, including reservoir screening, air injection design, ignition, and monitoring. The benefits and potential risks of HPAI are also discussed. Along with the discussion of design and operating criteria, the paper contributes significantly in the comparison of oxidation/combustion kinetics for light oils versus heavy oils (HPAI versus in situ combustion), as well as in a discussion of the oil mobilizing effects of a gas flood compared to an advancing thermal (combustion) front.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.028 |
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".