Visual Support for Heavy-Oil Emulsification and its Stability for Cold-Production using Chemical and Nano-Particles
Notice bibliographique
Résumé
Abstract The performance of non-thermal, cold, heavy oil production methods, such as waterflooding or gas injection (foamy oil) applications, is predominantly limited. As an alternative, efficient chemical flooding has been recommended and tested around the world (mainly in Canada and China). Cost aside, the main issue with this application is the compatibility of the chemicals used in terms of rock type, salinity, temperature, and emulsion generation and stability. Low-cost materials with strong emulsion stability capability have been tested previously in our research group. As an ongoing part of our past studies on the extensive chemical flooding applications in enhanced heavy oil recovery, we visualize directional motion, patterns, and deformation of fingers observed in Hele-Shaw cells with different oil types (heavy oil of 13,850 cP at 21°C from western Canada, heavy mineral oil of 649.9 cP at 20°C. Macroscopic and microscopic visualizations allow us to gain insights into important and fundamental physical flow mechanisms such as the Saffman-Taylor instabilities due to the viscosity ratio, and the Marangoni effect due to the surface tension gradient, wetting, dewetting, and superspreading behaviors. Hele-Shaw visualization studies in the past have mainly focused on weakening or eliminating the fingering instabilities. In this study, we attempt to categorize the observed finger types which appear during the displacement, identify the finger types responsible for heavy oil-in-water emulsification, and relate the visualization results to final enhanced heavy oil recovery. We observe both miscible and immiscible flow behavior and in the case of immiscible flow, and we investigate the impact of the capillary number on finger growth and ramification patterns by manipulating the flow rates. There are a plethora of factors that may impact the visualization of heavy-oil emulsification including the fixed chemical properties, chemical compatibility, heterogeneous (or non-heterogeneous) chemical reaction, capillary number effect, mobility ratio, IFT gradient, chemical concentration, liquid-substrate wettability, pH of liquids, precipitation, and brine conditions. To investigate such impact, we investigated a large series of in-situ heavy oil-in-water emulsifications at various conditions using emulsifiers such as anionic surfactants, cationic surfactants, and NaOH. And for the stabilization of the emulsions formed with the emulsifiers, we tested nanofluids (silica, cellulose nanocrystal, zirconia, alumina) and polymer (Xanthan Gum and an anionic polyacrylamide-based polymer). The results displayed that there exist finger types which are responsible for stable Winsor type 4 heavy oil-in-water emulsification. By the method of controlling the infrastructure of emulsion droplets and correlating observed multiple finger interactions to the material designs, we enable the selection of both novel and cost-effective designs for heavy oil recovery as well as displacement mechanisms.
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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 | 0,001 | 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) | 0,006 | 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 ».