Mechanistic analysis of flow functions in tertiary enriched gas injection following secondary lean gas injection
Bibliographic record
Abstract
Significant amounts of residual oil can remain trapped after primary and secondary recovery stages, which can be effectively recovered using tertiary gas injection processes. While tertiary gas injection in water-swept reservoirs has been widely studied, the characterization of tertiary enriched gas injection following secondary lean gas injection remains underexplored. One critical challenge is the lack of gas-oil relative permeability and capillary pressure functions specific to this process, which play a key role in controlling multiphase flow behavior and oil mobilization. To address this gap, a series of coreflood experiments was performed by injecting lean gas followed by enriched gas into a low-permeability carbonate core. The CMG/GEM compositional simulator, coupled with the Design Exploration Controlled Evolution (DECE) history-matching algorithm, was used to match experimental data, including oil recovery, cumulative gas production, and pressure drop. Relative permeability and capillary pressure curves for tertiary enriched gas injection were derived from these simulations. Results showed the ultimate oil recovery increased by 12%. Analysis of ternary diagrams and produced fluid composition indicated that residual oil was mobilized primarily through a combined vaporizing-condensing mechanism. This study demonstrates the potential of tertiary enriched gas injection as an effective recovery strategy for reservoirs subjected to prior lean gas flooding.
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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 | 0.000 |
| 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 | 0.001 |
| 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".