A review of surfactants as synergists in the field of 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
The efficient development of oil resources is significant for alleviating the increasingly serious contradiction between oil supply and demand and ensuring national oil security. Surfactant flooding technology is one of the essential methods to enhance oil recovery and has been widely used in oil and gas development. The screening and development of traditional surfactants for oil displacement are mainly focused on reducing interfacial tension based on the theory of capillary number while ignoring the emulsification effect of surfactants. For different types of residual oil/remaining oil, the main contradictions faced by the target reservoir should be thoroughly analyzed to clarify whether the performance of surfactants used for oil displacement is mainly to reduce interfacial tension or strong emulsification capacity, and then screen surfactants suitable for reservoir characteristics and crude oil properties. In this article, the principle and application of surfactant flooding technology are reviewed from two aspects of interfacial tension and emulsification performance, the interrelationship between interfacial tension and emulsification capacity is explored, and the evaluation indicators and existing problems of surfactant used for oil displacement are analyzed in detail. Most of the evaluation methods for emulsification in the past were based on water extraction rate. A method for evaluating the emulsification ability of surfactants to crude oil is introduced. It is expected to provide scientific guidance for the screening and development of surfactants for oil displacement.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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