Nanoparticles as Foam Stabilizer for Steam-Foam Process
Bibliographic record
Abstract
Summary Steam foams have been considered effective additives for unconventional oil-recovery processes. Conventionally, for steam-foam applications, chemical additives are injected with steam. However, this procedure can have serious challenges because of poor thermal stability of additives and high volume of additives loss caused by adsorption to the rock 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), a foam-height test using N2 at reservoir conditions, and thermal-stability analysis were designed to measure nanoparticle size distribution in brine, foamability, and thermal stability of the additive solutions, respectively. Subsequently, coreflooding tests were performed to evaluate the synergistic effect of nanoparticles and surfactants on the foam performance and oil recovery. We observed an optimal ratio of nanoparticle and surfactant that yields the best foam-generation performance in bulk medium. Herein, surface-treated silica nanoparticles have been tested with two of our candidate surfactants. The nanoparticles alone generate a small amount of foam, whereas each surfactant generates a small-to-moderate amount of foam. Synergy is demonstrated by the system that contains 0.1-wt% nanoparticles (the optimal concentration) and 0.5-wt% surfactant solution at neutral pH (≈7), as it leads to approximately 67 and 50% greater foam height, respectively, for Surfactants A and B than foam height observed in tests with surfactants only, in bulk medium. Corefloods with coinjected steam and water containing nanoparticles and surfactant confirm the synergy, exhibiting measurable improvement in mobility-reduction factor (MRF) and steam control, compared with coinjection 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".