Application of foam floods for enhancing heavy oil recovery through stability analysis and core flood experiments
Bibliographic record
Abstract
This work concerns the investigation of foam application to enhance heavy oil recovery through stability analysis tests, core floods, and data assessment. Monitoring transient behaviour of foam heights in a glass column showed that salt/alkaline concentration/type significantly affects the stability of foam. Meanwhile, the ionic nature of the surfactant and gas type also plays a crucial role. Therefore, the foam stability analysis should be helpful for the successful design of foam floods in enhanced oil recovery (EOR) processes. The surfactants that gave the most stable foam were used in core floods, and found that an increase in surfactant alternating gas (SAG) ratio decreases the oil recovery; furthermore, the efficiency of Cetrimonium Bromide (CTAB) is lower than that of Sodium Dodecyl Sulfate (SDS) in a sandstone core. Finally, the Leverage approach was used to assess the obtained data points of stability as well as core flooding tests through the developed least square supported vector machine (LS‐SVM) models. It confirms the validity of experimental data.
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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".