An Analysis of Reservoir Production Strategies in Miscible and Immiscible Gas Injection Projects
Bibliographic record
Abstract
Successful design and implementation of a miscible gas injection project depends upon the minimum miscibility pressure (MMP) and other factors such as reservoir and fluid characterization. The experimental methods available for determining MMP are both costly and time consuming. Therefore, the use of correlations that prove to be reliable for a wide range of fluid types would likely be considered acceptable for preliminary screening studies. This work includes a comparative evaluation of MMP correlations and thermodynamic models using an equation of state by PVTsim software (Schlumberger, 2001a). We observed that none of the evaluated MMP correlations studied in this investigation is sufficiently reliable. EOSbased analytical methods seemed to be more conservative in predicting MMP values. Following an acceptable estimate of MMP, several compositional simulation runs were conducted to determine the sensitivity of the oil recovery to variations in injection pressure (at pressures above, equal to and below the estimated MMP), stratification and mobility ratio parameters in miscible and immiscible gas injection projects. Simulation results indicated that injection pressure was a key parameter that affects oil recovery to a high degree. MMP determined to be the optimum injection pressure. Stratification and mobility ratio could also affect the recovery efficiency of the reservoir in a variety of ways. Key words : Reservoir production; Miscible gas injection; Immiscible gas; Minimum miscibility pressure
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".