Worm‐like micelles: A new approach for heavy oil recovery from fractured systems
Bibliographic record
Abstract
In this work, a new type of flooding system, “worm‐like micelles”, in enhanced heavy oil recovery (EOR) has been introduced. Application of these types of surfactants, because of their intriguing and surprising behaviour, is attractive for EOR studies. Fundamental understanding of the sweep efficiencies as well as displacement mechanisms of this flooding system in heterogeneous systems especially for heavy oils remains a topic of debate in the literature. Worm‐like micellar surfactant solutions are made up of highly flexible cylindrical aggregates. Such micellar solutions display high surface activity and high viscoelasticity, making them attractive in practical applications for EOR. In this study, worm‐like micellar solutions were used for flooding experiments in micromodels, initially saturated with heavy crude oil. The fractured micromodels with different fracture geometrical properties, different orientation angles and length, were used in the tests under oil‐wet condition. During experiments, high quality pictures of injection processes were recorded. Oil recoveries as a function of injected pore volumes and microscopic mechanisms during displacements were investigated from precise analyses of the provided pictures. It was observed that three mechanisms govern the EOR process during worm‐like micellar solution flooding: ultra‐low interfacial tension, high viscosity of the injecting fluid and in situ formation of macro‐emulsion. Considering these mechanisms, worm‐like micellar surfactants solutions are potentially good choices for EOR in heterogeneous systems such as fractured reservoirs. This study illustrates that the application of worm‐like micelles for heavy oil recovery in heterogeneous systems can reduce the risks involved with heterogeneity on flooding performance in such reservoirs.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".