Novel Large-Hydrophobe Alkoxy Carboxylate Surfactants for Enhanced Oil Recovery
Bibliographic record
Abstract
Summary A new class of surfactants has been developed and tested for chemical enhanced oil recovery (EOR) that shows excellent performance under harsh reservoir conditions. These novel Guerbet alkoxy carboxylate (GAC) surfactants fulfill this need by providing large, branched hydrophobes; flexibility in the number of alkoxylate groups; and stability in both alkaline and nonalkaline environments at temperatures up to at least 120°C. The new carboxylate surfactants were recently manufactured at a cost comparable to other commercial EOR surfactants by use of commercially available feedstocks. A formulation containing the combination of a carboxylate surfactant and a sulfonate cosurfactant resulted in a synergistic interaction that has the potential to reduce the total chemical cost further. One can obtain both ultralow interfacial tension (IFT) with the oils and a clear aqueous solution (even under harsh conditions such as high salinity, high hardness, and high temperature with or without alkali) with these new large-hydrophobe alkoxy carboxylate surfactants. Both sandstone and carbonate corefloods were conducted, with excellent results. Formulations were developed for both active oils (contains naturally occurring carboxylic acids) and inactive oils (oils that do not produce sufficient amounts of soap/carboxylic acid), with excellent results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".