Miscible Gas Injection: A Successful Experience Leading to Proper Reservoir Management Through Simulation Study A Carbonate Case Study
Bibliographic record
Abstract
Abstract Miscible gas displacement is known as one of the most efficient EOR methods throughout the world. The mechanism is seem to be promising in homogeneous sandstones but is quite challenging when it comes to heterogeneous carbonates. The studied field is among the tight heterogeneous carbonates from southwest of Iran. The reservoir in an undersaturated high pressure oil reservoir with no active aquifer. Due to low permeability of the reservoir, a miscible gas injection was thought to be efficient in increasing the oil recovery, maintaining the reservoir pressure and increasing the production plateau. A full compositional simulation model was built taking into account reservoir heterogeneities. The simulation study includes history matching of the past reservoir performance, optimization of the gas injection well number and location, and optimization of gas injection and oil production rates. After running sensitivity analysis, the reservoir engineers came up with drilling of the 6 crestal gas injection wells. In Addition, the water injection was thought as an alternative for EOR purpose but it didn't show up well and was given up. The outcome of this simulation study nominates the miscible gas injection as the most promising method concerning the reservoir management purposes as it increases the recovery from 16% to around 42% through 40 years of production and could maintain the plateau of 120000 bopd up to 19 years from the production start.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".