Experimental investigation and modelling of CO<sub>2</sub>‐foam flow in heavy oil systems
Bibliographic record
Abstract
In this work, the C 14‐16 alpha 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 CO 2 and 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 ( F MR ) 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 ( C g /C c ) 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 cm 3 /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 ( F MR ) to different degrees with F MR ‐ without oil / F MR ‐ with oil ranging 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".