Nanoparticles as Foam Stabilizer for Steam-Foam Process
Bibliographic record
Abstract
Abstract Steam foams have been considered as effective additives for unconventional oil recovery processes. Conventionally for steam-foam applications, chemical additives are injected with steam. However, this operation has serious drawbacks owing to poor thermal stability of additives and high volume of additives loss, due to adsorption to rocks surface. To overcome these limitations, nanoparticles can be used as novel additives to improve generation and stabilization of the foams for steam-foam applications. In this study, silica nanoparticles in synergy with surfactants have been used as steam additives. Dynamic light scattering (DLS), foam height test using N2 at reservoir conditions, and thermal stability analysis were designed to measure nanoparticles size distribution in brine, foam-ability and thermal stability of the additive solutions, respectively. Subsequently, core-flooding tests were performed to evaluate the synergistic effect of nanoparticles and surfactants on the foam quality and oil recovery. We observe an optimum ratio of nanoparticle and surfactant that yields the best foam generation performance (maximum foam height). Herein, surface treated silica nanoparticles have been tested with two of our candidate surfactants. The nanoparticles alone generate small amount of foam, while each surfactant generates a small to moderate amount of foam. Synergy is demonstrated by the system which contains 0.1 wt% nanoparticles (the optmum concentration) and 0.5 wt% of surfactant solution at neutral pH (~7), as it leads to about 67% and 50% greater foam height respectively for Surfactants A and B than observed in tests with surfactants only. Core-floods with co-injected steam and water containing nanoparticles and surfactant confirm the synergy, exhibiting significant improvement in mobility factor reduction (MRF) and steam control, compared to co-injection of steam and water containing only surfactant.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".