Effect of Oil and Flue-Gas Compositions on Oil Recovery in the Flue-Gas/Light-Oil Injection Process
Bibliographic record
Abstract
Abstract Flue gas injection is becoming more attractive as a feasible and environmentally friendly process for improving oil recovery from light oil reservoirs. When obtained from surface sources, the flue gas process has an added advantage of preventing carbon dioxide (CO2), a greenhouse gas, from being disposed into the atmosphere. Flue gas can also be generated in-situ by the spontaneous ignition of oil when air, a readily available gas, is injected into high temperature, high pressure (light oil) reservoirs. The availability of flue gas and/or air and the observed high oil recovery potential make the flue gas process an economically attractive process. Oil recovery efficiency and displacement characteristics were studied in the laboratory with nitrogen and two flue gas compositions having CO2 content of 16% and 30% and two light oils obtained from two different reservoirs. The recombined light oils were displaced by the flue gases in a 2.44 m long, 5.1 cm diameter Berea sandstone core at irreducible brine saturation. The studies were conducted at reservoir pressures up to 41.58 MPa and temperatures of 80.6°C and 116°C, corresponding to the reservoir temperature of the oils studied. The experimental data was history-matched with a fully compositional simulator and the results show that oil recovery efficiency has a direct relationship to the paraffin and naphthene content of the oil. It was found during the core flood that the displacement of the oil with lower paraffin (higher naphthene) content produced more oil than the displacement with higher paraffin at one pore volume of flue gas injected, even at lower operating pressure.
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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".