Effective Extraction of a Heavy Oil Resource by an Environmentally Friendly Green Solvent: Limonene.
Notice bibliographique
Résumé
Abstract Global oil consumption is predicted to increase by 15% from 2021 to 2050. The increasing oil demand and decreasing conventional oil supply force us to find alternate energy supplies. The key to this problem lies with the vast untapped heavy oil and bitumen resources. In this study, we investigate the effectiveness of an environmentally friendly solvent, limonene, in recovering heavy oil. Three core flood experiments representing three different recovery methods were carried out. These include steam flooding (E1), solvent flooding (E2), and solvent-steam co-injections (E3). The green solvent, limonene, is a citrus-based non-toxic solvent. It was chosen due to its high organic solvency and ready availability. Throughout the experiments, steam was injected at a cold water equivalent of 18 ml/min, while limonene was injected at 2 ml/min. The experiments were run with a back pressure of 45-55 psi. The core pack was prepared by filling the pore space of Ottawa sand with a 60% heavy oil sample and 40% water by volume (including water percentage in oil). Produced oil and water samples were collected every 20 min during the experiments. These samples were further analyzed by emulsion characterization to determine emulsion stability and oil quality. Spent rock analyses were done to calculate the displacement efficiency of each of the experiments. In addition, an economic analysis was done to determine the optimal recovery method. Spent rock analysis showed that a sole injection of limonene (E2) had the highest oil recovery. This confirms the high organic solvency of limonene achieved miscible flooding producing about 46 vol % from a total of 60 vol % initial oil. Steam flooding (E1), on the other hand, did not perform as well, producing around 29 vol %. The post-mortem sample from E1 indicated asphaltene precipitation which could have lowered oil recovery. Co-injection of limonene and steam was expected to yield the highest recovery due to the presence of two active drive mechanisms, thermal and miscible flooding. However, it performed comparatively less (41 vol %) than a sole injection of limonene (E2). This is further explained with emulsion characterization results. Experiments involving steam (E1 and E2) revealed strong emulsions in the oil produced, indicating a lower quality. Furthermore, it was seen that the solvent-steam process produced weaker emulsions compared to steam flooding alone. On the other hand, solvent flooding (E2) produced high-quality oil with little to no emulsions. These results along with the economic analysis, indicate that the optimal recovery method would be solvent flooding (E2). Our results prove that limonene is a promising organic solvent. Limonene is non-toxic, readily available, and safe to handle. As a result, it can be a safe green alternative to commonly used toxic organic solvents such as toluene.
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 ».