Novel surfactant delivery system for controlling surfactant adsorption onto solid surfaces. Part II: Dynamic adsorption tests
Bibliographic record
Abstract
Abstract During surfactant flooding in enhanced oil recovery (EOR), surfactant adsorption onto rock surfaces constitutes a serious shortcoming because as the chemical slug flows through the oil reservoir, surfactant is lost to adsorption before it reaches the target residual oil saturation zones, which renders the process technically inefficient and uneconomical. This work is the second phase of a wider study aimed at the evaluation of a new technology to control surfactant adsorption onto solid surfaces by using a surfactant delivery system based on surfactant/β‐CD inclusion complexes. This complexation was confirmed through surface tensiometry, 1 H NMR and FTIR spectroscopy, and microscopic studies (TEM and SEM). The performance of the surfactant delivery system in controlling surfactant adsorption was evaluated through dynamic adsorption tests using different mixtures of solid adsorbents (sandstone, shale, and kaolinite). Dynamic adsorption data show reductions of surfactant adsorption ranging from 50–92 % for all the adsorbents tested; therefore, the surfactant delivery system seems to be highly effective in inhibiting surfactant adsorption onto solid surfaces. This new technology has great potential for EOR chemical flooding applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".