Miscible flooding Performance of Terpenes as Green Solvents for Heavy Oil Recovery
Notice bibliographique
Résumé
The vast reserves of heavy oil present a possible solution to the world’s energy demands, but the challenge of low mobility due to high viscosity hinders their extraction. There are two methods that can be used to enhance their extraction. The methods are heat introduction and chemical injection. Steam injection is the most reliable heat introduction method but its usage of large amounts of fresh water, emission of significant amounts of carbon dioxide, and heat losses makes its usage a major drawback. \nOn the other hand, solvents are the most effective chemicals to reduce viscosity and enhance mobility, but are costly for use in the field, toxic and difficult to handle and transport to remote locations. Thus, this research investigates the feasibility and effectiveness of environmentally friendly solvents. \nFive core flooding experiments were conducted on a heavy oil sample. 16.49 API gravity and 140,000 cP viscosity heavy oil sample was blended at 60% initial oil saturation with 39% porosity Ottawa sand. 40% of the pore volume was filled with distilled water. Performance of four environmentally friendly solvents (d-limonene, turpentine, citronella and beta-pinene) was compared with toluene on this heavy oil. All solvents were injected at 2 mL/min flow rate. Produced oil qualities were further examined in terms of water content (water-in-oil emulsions) by using an optimal microscope. Later, the amount of solvent and water in produced oil samples were determined through Thermogravimetry Analysis / Differential Scanning Calorimetry (TGA/DSC) analysis. Compositional analysis on produced oil samples was carried out through Saturates, Aromatics, Resins, Asphaltene (SARA) fractionation. To better understand the performance differences in each solvent use, viscosity measurements were conducted on solvent-heavy oil blends at varying solvent doses. Finally, an economic analysis was conducted on each solvent to understand their feasibility. \nIn all experiments, high oil recovery was obtained because all solvents used in this study were miscible with the heavy oil used in this study. Hence, first-contact miscibility was maintained in all experiments. Toluene yielded the highest oil recovery with the highest quality due to its greatest solvency power among all solvents used in this study. However, due to its toxicity, most oil companies in the United States will not implement toluene for use in their oil recovery projects. D-limonene and beta-pinene solvents gave comparable results with toluene, with only 5% lower recovery but less environmental impact. SARA analysis shows that, citronella resulted in the least asphaltene content and greatest aromatic content. This proves that citronella resulted in the best quality of oil produced. Viscosity measurements on varying solvent-heavy oil concentration shows that even at lower concentration of each environmentally friendly solvent, similar mobility enhancement was achieved on heavy oil when compared to toluene-heavy oil blends. This is because of the molecular structures of terpenes. These structures are known as degreasers and reduces the crude oil viscosity significantly. \nThis study shows that terpenes are effective and feasible green solvents that can be used for heavy oil recovery. Because this study is the first study conducted on terpenes, it brings new insight to heavy oil production with an environmentally friendly way.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».