Effect of Alkalis on Phase Behavior of Mixtures between Single and Double Tail Anionic Surfactants
Bibliographic record
Abstract
Abstract Alkaline–Surfactant–Polymer (ASP) flooding is considered as a promising method for enhanced oil recovery since 1980’s. Even though the concept of ASP is straight forward, but it is a very complicated process due to complex of chemical slug. Moreover, the success of ASP is mostly demonstrated in laboratory conditions; thus, it is necessary to investigate this process in hostile reservoir conditions for achieving a wider and successful implementation. This paper presents a comprehensive evaluation of the phase behavior of ASP systems, which is successful, the key of ASP process in high temperature reservoir. The experimental results prove that the mixtures of single tail anionic surfactant and double tail anionic surfactant would be adequately overcome the challenge of high temperature condition of reservoirs. These mixtures are also more compatible with polymer and increase the optimum salinity. In other words, the developing of new advanced surfactant mixture allows apply ASP flooding in high salinity reservoir. Next, we examine the important role of alkaline in ASP process. Three alkalis such as including sodium carbonate, sodium metaborate, and sodium metasilicate were tested in phase behavior experiments at high temperature condition (90°C) for both synthesis (dodecane) and crude oils. The results indicate that alkaline in the ASP systems can react with some acidic components of the crude oil to form in-situ surfactant, which then would help lowering the IFT between oil and water phases. It reduces the cost of process because alkaline is cheaper than synthesis surfactant. Besides alkaline also affect on interfacial tension and phase behavior of ASP systems.
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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".