Detection of the Onset of Asphaltene Precipitation in a Heavy Oil-Solvent System
Notice bibliographique
Résumé
Abstract When a hydrocarbon solvent is made in contact with a heavy oil under a sufficiently high reservoir pressure, asphaltene precipitation occurs so that the heavy oil is in-situ upgraded during a solvent-based heavy oil recovery process. Some physicochemical properties of this in-situ upgraded heavy oil are rather different from those of the original crude oil in the heavy oil reservoir. In this paper, a series of saturation tests is conducted for a heavy oil-propane system under different saturation pressures in a see-through windowed high-pressure saturation cell with six sampling ports at different vertical locations. The onset of asphaltene precipitation is determined by measuring and comparing several physicochemical properties (e.g., the solubility, oil-swelling factor, density, viscosity, and asphaltene content) of the propane-saturated and flashed-off heavy oils taken from different parts of the propanesaturated heavy oil under each saturation pressure. It is found that when the heavy oil is saturated with propane at P ? 780 kPa, the respective properties of the solvent-saturated and flashed-off heavy oils taken from the upper and lower parts are different to large extents. This may be because asphaltene aggregation occurs in the solvent-saturated heavy oil and some heavy components move downward to the bottom of the saturation cell. If the saturation pressure is increased to P=850 kPa, asphaltene precipitation occurs and some large asphaltene particles are deposited onto the acrylic windows of the saturation cell. Although the physicochemical properties of the solvent-saturated and flashed-off heavy oils measured by using different experimental methods show variable sensitivities to the asphaltene precipitation, its onset can be successfully detected in practice. Introduction Western Canada has tremendous heavy oil and bitumen deposits with estimated original-oil-in-place (OOIP) of 2.5 trillion barrels[1]. They hold great potential to meet the future hydrocarbon fuel demand, while the conventional petroleum reserves are being depleted. Nevertheless, how to effectively and economically recover heavy oil and bitumen remains a technical challenge due to their extremely high viscosities. At present, thermal-based heavy oil recovery methods are often applied because they can dramatically reduce the heavy oil viscosity. However, large heating and water source requirements, heat losses to thin oil formations, and water treatment cost make these tertiary oil recovery methods ineffective and uneconomical. Solvent-based heavy oil recovery processes[2–7] have recently gained more and more attention because of their distinct advantages over the thermal-based heavy oil recovery methods. In a typical solvent-based heavy oil recovery process, such as vapour extraction (VAPEX) process, gaseous condensable solvents[8], together with non-condensable carrier gases[9], are injected and dissolved into the heavy oil to dramatically reduce its viscosity. In some cases, the heavy oil viscosity reduction due to sufficient solvent dissolution may be comparable to that achieved by applying the thermal-based heavy oil recovery methods. Another major advantage of the solvent-based heavy oil recovery processes is asphaltene precipitation through sufficient solvent dissolution so that the heavy oil in the reservoir is insitu upgraded. The precipitated asphaltenes are deposited onto the sand grains and thus left behind in the reservoir. The produced heavy oil has a much lower viscosity and better quality in comparison with the original crude heavy oil[10, 11].
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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 | 0,000 |
| 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) | 0,000 | 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.
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