Microemulsion Formulations with Tunable Displacement Mechanisms for Heavy Oil Reservoirs
Notice bibliographique
Résumé
Abstract Waterflooding of heavy oil reservoirs is commonly used to enhance their productivity. However, preferential pathways are quickly developed in the reservoir due to the significant difference in viscosity between water and heavy oil, and hence, the oil is trapped. Here, we propose a platform for designing ultra-low IFT solutions for reducing the capillary pressure and mobilizing the heavy oil. In this study, mixtures of organic acids and bases were formulated. Three different formulations were tested: (i) Ionic liquid (IL) formulation where bulk acid (4-dodecylbenzene sulfonic acid) and base (Tetra-N-butylammonium hydroxide) were mixed using general protocols for ILs synthesis, (ii) Acid-Base solution (ABs) where the acid (4-dodecylbenzene sulfonic acid) and base (Tetra-N-butylammonium hydroxide) were mixed in low weight fractions directly in water, and (iii) Acid Salt-Base solution (ASBs) where the acid salt (sodium dodecylbenzene sulfonate, SDBS) was used instead of the acid. All the formulations have a 1:1 stoichiometric ratio of acid and base. Salinity scans were conducted to determine the optimum salinity that gives the lowest IFT for each formulation. Corefloods were conducted in hydrophilic and hydrophobic sandpacks to evaluate the three formulations at their optimum salinities for post-waterflood heavy oil recovery. The IL and ABs formulation are acidic solutions with pH around 3. The ASBs formulation is highly basic with a pH around 12. Non of the formulations salted out below 14 wt% of NaCl. While conventional surfactant, SDBS, precipitated at salt concnetration less than 2 wt% of NaCl. The formulation solutions (1 wt%) have different optimum salinities: 2.5 wt% NaCl for ASBs, 3 wt% NaCl for IL and AB. Although IL and AB have the same composition and molar ratio of the components, their performances are completely different, indicating different intermolecular interactions in both formulations. Corefloods were conducted using sandpack saturated with Luseland heavy oil (~15000 cP) and at fixed Darcy velocity of 12 ft/day. A slug of 1 PV of each formulation was injected after waterflooding for 5 PV and followed by 5 PV post-waterflooding. In the hydrophilic sandpacks, IL and AB formulation produced an oil bank, consisting mainly of W/O emulsion, with oil recovery that is 1.7 times what was recovered by 11 PV of waterflooding solely. Majority of the oil was recovered in the 2 PV of waterflood following the IL slug. ASBs formulations produced O/W emulsions with prolonged recovery over 5 PV waterflooding after the ASB slug. The recovery factor for ASBs was 1.6 times that recovered for 11 PV of waterflooding only. In the hydrophobic sandpacks, The ASB formulation slightly increased the recovery factor compared to only waterflooding. While for IL and AB formulation, the recovery factor decreased. This work presented a novel platform for tuning the recovery factor and the timescale of recovery of heavy oil with a variable emulsion type from O/W to W/O depending on the intermolecular interactions in the system. The results demonstrate that the designed low IFT solutions can effectively reduce the capillary force and are attractive for field application.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 ».