Preliminary Screening and Formulation of New Generation Nanoparticles for Stable Pickering Emulsion in Cold and Hot Heavy-Oil Recovery
Notice bibliographique
Résumé
Summary Injecting water with chemicals to generate emulsions in the reservoir is a promising method in the enhancement of heavy-oil recovery because the formation of oil-in-water (O/W) emulsions significantly reduces oil viscosity. Nanoparticles (NPs) (Pickering emulsions) can be used for this purpose as a cost-effective alternative to expensive surfactants; however, such Pickering emulsions need to be stable for successful applications. The objective of this study is to screen the effective emulsifier for O/W emulsions from a broad range of solid NPs and identify suitable Pickering emulsifying agents (e.g., adjusting pH or salt concentration) that can render emulsions stable at relevant conditions, and to investigate how a range of physical parameters, such as particle concentration, water/oil ratio (WOR), and temperature affect emulsion stability. Five NPs—including cellulose nanocrystals (CNCs), silica, alumina, magnetite, and zirconia—were tested on their capabilities of stabilizing O/W emulsions through glass vial screening tests under various pH and salinity conditions. The screening results showed that the CNC could become an effective emulsifier by either adjusting pH or salinity. In addition, zeta potential measurements were conducted to explain the observations. The stabilization mechanisms of CNCs were studied through an epifluorescent transmitted microscope showing that the formation of a dense particle layer around the oil droplets, as well as a network in the continuous phase, were the two main mechanisms accounting for the high stability of the emulsions stabilized by CNCs. The effects of particle concentrations on the emulsion stability were studied quantitatively by analyzing the droplet-size distributions calculated by the open-source ImageJ software, with the results showing a sharp decrease in droplet size, followed by a smooth change as the particle concentration increased. For the WOR effect, phase inversion from O/W to water-in-oil (W/O) emulsions was observed when the oil content was more than 0.6. The thermal stability of emulsions was studied both macroscopically by glass bottle tests and microscopically through a microscope, both of which show that the CNC-stabilized emulsions remained thermally stable up to 100°C. The rheological properties of both aqueous dispersions of CNCs and the corresponding O/W emulsions were also measured under various salinity conditions. The results showed that the salinity had a great impact on the viscosity of the CNC suspension and the typical shear-thinning behavior of Pickering emulsions. This study provides an option to enhance emulsion stability without surfactants, which will reduce the costs and facilitate field applications of emulsion flooding in heavy-oil recovery.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».