Numerical and Laboratory Assessment of the Oil-Recovery Mechanisms in High-Pressure Air Injection (HPAI) Process
Bibliographic record
Abstract
Abstract High Pressure Air Injection (HPAI) as an EOR process has found application in high-pressure, light-oil reservoirs, and is particularly promising for low water injectivity reservoirs. Successful HPAI projects have been reported over the last twenty years. However, there is an ongoing discussion on the manner in which the process operates. Several works have pointed out that HPAI can be assimilated to a flue gas drive, since the thermal and associated effects of the oil oxidation would have negligible effect on the overall performance. Meanwhile, calorimetric and combustion tube tests have been used to characterize the operation of HPAI at reservoir conditions. These results are then applied in field-scale simulation models to predict the recovery scenario. Although it is clear that a combination of both gas drive and oxidation reaction effects drives the oil out of the reservoir, the contribution of each process is yet to be quantified. This work presents a methodology involving experimental tests and reservoir simulation to build a proper simulation model for HPAI at laboratory conditions and quantify the effect of flue gas drive and oxidation reactions on the total oil recovery. Calorimetric, PVT, flue gas floods and combustion tube tests are available for two light oils. This data is used to extract information about the recovery of each driving mechanism via reservoir simulation. Two- and three-phase (light oil/flue gas/water) displacements are conducted in a foot-long Berea Sandstone core to analyze the effect of saturation history, pressure, temperature and phase composition on the oil recovery at typical process conditions. Same oil species as in the rest of the experimental data are employed. A thermal simulation model for combustion tube tests is built based on the information from individual experiments, and refined by history matching. Reservoir simulation of a combustion tube test by incorporating history-matched relative permeability curves shows that a flue gas drive process alone cannot explain the high recovery seen in the laboratory.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".