Novel Large-Hydrophobe Alkoxy Carboxylate Surfactants for Enhanced Oil Recovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it