Recent Technology Developments in Surfactants and Polymers for Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract Several classes of new surfactants have recently been tested for enhanced oil recovery. These new surfactants were needed for oil field applications under reservoir conditions that made it difficult or impossible to find conventional surfactants with the desired behavior such as ultra-low interfacial tension, aqueous stability, thermal stability at high temperature, low retention, tolerance to high salinity and so forth. We illustrate results for several of these new surfactants and discuss under what conditions they are suitable, how we developed formulations using them and some of the general principles that can be applied to future applications. A common theme of this development is the need for surfactants with large hydrophobes (carbon numbers above 18) even for some light oils. A second common theme is the advantages and flexibility of propylene oxide and ethylene oxide linkages between these large hydrophobes and the sulfate or carboxylate end group. A third common theme is the advantages of highly branched hydrophobes regardless of the other characteristics of the surfactant structure help prevent undesirable viscous phases. Finally, a fourth common theme is the advantages of using surfactant mixtures with diverse structures and sizes. These common elements enable us find surfactant formulations that are highly effective and that can be made from available feedstocks at reasonable cost.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".