Effect of Non-equilibrium Gas Injection on the Performance of (Immiscible and Miscible) Gas–Oil Gravity Drainage in Naturally Fractured Reservoirs
Bibliographic record
Abstract
In naturally fractured reservoirs, improving the matrix–fracture interactions is critical to the success of the applied improved oil recovery method. Therefore, a study of the mechanisms that control the mass exchanges between fracture and matrix can help to optimize recovery. This paper concerns an experimental and simulation study to investigate the performance of the gas–oil gravity drainage process in naturally fractured reservoirs. In this study, five gas injection experiments were conducted at different miscibility (i.e., immiscible and fully miscible) conditions using CO 2, N 2, and a synthetic flue gas composed of 20% (v/v) CO 2 and 80% (v/v) N 2 . The impact of switching from an immiscible (i.e., nitrogen or flue gas) gas to a non-equilibrium and fully miscible CO 2 injection is investigated. The effect of miscibility on the block–block interaction is also examined using a stacked core with an impermeable barrier. The results reveal that injection of non-equilibrium gas with higher solubility in the oil phase results in a zone of decreased oil viscosity, which leads to an improved gravity mediated recovery. The results also show that the ultimate oil recovery increases considerably once miscibility is achieved. A numerical model is implemented to perform compositional simulations of multiphase, multicomponent gas injections at different miscibility conditions. Agreement of the developed model with the experimental results indicates that gravity drainage, capillary holdup, and mixing are the main controlling mechanisms.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".