The Effect of Solvent based Nanofluid Flooding on Heavy Oil Recovery
Notice bibliographique
Résumé
Abstract The objective of this study is to investigate the effect of solvent based nanofluid flooding followed by waterflooding on heavy oil recovery performance and sweep efficiency improvement in unconsolidated oil-wet porous media. The sweep efficiency improvement is correlated with in-situ emulsion generation during such displacement and is monitored with a CT scanner. Partially hydrophobic silica NPs with proper interface affinity was firstly synthesized, and subsequently dispersed in toluene along with a surfactant to prepare the solvent based nanofluid. The solvent based nanofluid was then injected into an oil saturated sandpack at different slug sizes and further chased by brine to investigate the sweep efficiency improvement through possible emulsification. To investigate the beneficial effect of NPs, the aforementioned flooding was compared against conventional solvent flooding followed by waterflooding. The displacement patterns were monitored with a CT-scanner to assess the heavy oil recovery performance in the absence or presence of the solvent based nanofluid. The fluid density profiles during solvent based NF flooding followed by waterflooding were extracted from CT-scan images and compared against conventional solvent flooding followed by waterflooding to assess potential sweep efficiency improvement. The results revealed an increase in sweep efficiency during solvent based NF flooding followed by waterflooding. This was related to the creation of in-situ emulsion at the water-diluted oil interface due to the presence of NPs and surfactant in diluted oil. Unlike conventional solvent flooding where post water created several fingers through porous media, the water phase in solvent based nanofluid flooding was able to displace all the lower resistance paths created by the solvent based nanofluid. Moreover, chase water was able to recover almost all of the solvent in solvent based NF flooding, which is essential for the process to be economically viable. In general, the presence of solvent based NF was found to be effective in re- distributing the preferential water flow paths in to a more uniform front, which suppressed the fingering in turn and resulted in later water breakthrough, better sweep efficiency, and higher oil recovery. The proposed solvent based nanofluid flooding has not been tested so far as a heavy oil recovery technique. Unlike conventional solvent flooding which can suffer from high residual solvent to waterflooding, this technique revealed negligible residual solvent upon waterflooding. Moreover, it was found to be effective in sweep efficiency improvement and heavy oil recovery compared to conventional solvent flooding.
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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 ».