Experimental Study of the Interaction between NaOH, Surfactant, and Polymer in Reducing Court Heavy Oil/Brine Interfacial Tension
Bibliographic record
Abstract
The effect of the surfactant, NaOH, and polymer and the interactions between them on the heavy oil/water interface are unveiled by studying the dynamic interfacial tension (IFT), minimal transient IFT, and total organic carbon (TOC) and analyzing the phenomenon during the measurement of IFT of heavy oil/different alkaline systems, including alkaline (A), alkaline–surfactant (AS), alkaline–polymer (AP), and alkaline–surfactant–polymer (ASP). The results show that there exists a minimum transient IFT. There is an optimal composition to achieve the minimal IFT with varying NaOH concentrations in 0.018–0.8 wt %. For different chemical solutions, the optimal composition is different. Adding polymer affects the IFT by influencing the diffusion of species to or from the interface. Despite polymer addition, adding surfactant will increase the IFT at a lower alkaline concentration because of its competitive adsorption with OH – and reduce the IFT at a higher alkaline concentration because of its synergistic effect. The synergy between the surfactant and alkaline is turned out as follows: NaOH reacts with the polar components in the oil phase to produce ionized surface-active species; then the IFT is reduced; and the oil drop is prolonged. Surfactant accelerates the diffusion of ionized species from the interface to the bulk phase, and then the polar components underneath it are exposed to NaOH; therefore, the reaction between NaOH and polar oil components can proceed to further reduce the IFT. The contraction of the oil drop after surface-active species departed can be explained reasonably by considering the influence of the composition and structure of heavy oil.
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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".