Experimental investigation and modelling of CO<sub>2</sub>‐foam flow in heavy oil systems
Bibliographic record
Abstract
In this work, the C14‐16alpha olefin sulphonate (AOS) surfactant, octylphenol ethoxylate (TX‐100), and methyl bis[Ethyl(Tallowate)]‐2‐hydroxyethyl ammonium methyl sulphate (VT‐90) surfactant were selected as representatives of anionic, nonionic, and cationic surfactant to stabilize foam. The effects of surfactant concentration and gas/liquid injection rates on foam performance were examined by performing a series of oil‐free foam flow tests by injecting CO2and a foaming surfactant simultaneously into sandpacks. Foam flooding was conducted as a tertiary enhanced oil recovery (EOR) method after conventional water flooding and surfactant flooding. Furthermore, a new method was proposed to determine the residual oil saturation. The foam stability in the presence and absence of heavy oil was studied by a comparative evaluation of the mobility reduction factor (FMR) in both cases. The foam fractional flow modelling by Dholkawala and Sarma[36]was modified based on experimental results obtained in this study. The range of the ratio of two important model parameters (Cg/Cc) at various foam qualities was determined and could be used for large‐scale predictions. The results showed that during the oil‐free foam displacement experiments higher foam apparent viscosities ( ) were attained at lower gas flow rates and the maximum was attained at a total gas and liquid injection rate of 0.25 cm3/min with a gas fractional flow ratio of 0.8 for the foam in the absence of oil. The presence of oil reduced the foam mobility reduction factors (FMR) to different degrees withFMR‐without oil/FMR‐with oilranging from 4.25–13.69, indicating that the oil had a detrimental effect on the foam texture. The foam flooding successfully produced an additional 8.1–21.52 % of OOIP, which can be attributed to the combined effect of increasing the pressure gradient and oil transporting 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".