Underlying Physics of Heavy Oil Recovery by Gas Injection: An Experimental Parametric Analysis when Oil Exists in the Form of Oil Based Emulsion
Notice bibliographique
Résumé
Abstract Heavy oil in reservoirs exists in the form of either water in heavy oil (w/ho) emulsions after primary production under water drive, or during secondary recovery methods such as water or steam injection. In many cases, the decision to apply any secondary or tertiary methods such as CO2 or CH4 injection depends on the understanding of the behavior of these gases in w/ho emulsions at reservoir conditions. Such an understanding can reduce the uncertainties in reservoir modeling by providing an adequate fluid model for reservoir simulation and history matching studies. In this paper, we focus on the interfacial properties, relative volume change, and PVT behavior of CO2 and CH4 in (w/ho) emulsions. We first generated the (w/ho) emulsion using steam at 150oC. Next, the stability of our emulsion was tested using different criteria such as phase separation, viscosity of the produced emulsion compared with that of the starting oil, and the size and number of water droplets in the continuous medium. The experiments were run using two types of heavy oils that are collected from two representative fields in eastern Alberta, type A oil (27,000 cP) and type B oil (4,351 cP). A sensitivity analysis was performed to determine the impact of different operational variables such as water content in the emulsion, water pH, and flow rate; additionally, the role of asphaltene and resin in emulsion stability was investigated. The influence of water content in the emulsion was found to be critical and thus subsequent IFT and relative volume measurements as well as PVT analyses were conducted using emulsions of different water contents with a vol.% range from 10-70. The results were compared with a dead oil (no water) case. Two types of gases typically used to improve recovery in Alberta were tested: CO2 and CH4. IFT and volume measurements indicate the existence of critical water content which dramatically changes the behavior of the system; generally, emulsions with water content below this critical value exhibit lower IFT than the original oil, and the IFT falls steadily as the water content increases. The trend is reversed when the water content exceeds the critical value and IFT starts increasing before it stabilizes. This process happens when the water content reaches a vol.% higher than 50; however, it remains below that of the original oil. Regarding volume ratio, there seems to be a clear relationship between pressure and volume ratio of the emulsion and CO2 system. Overall, volume ratio increases as pressure increases regardless of water content. In general, for experiments run with CO2, data suggests that water content affects the rate of expansion, but ultimately the final volume ratio remains the same. The results of this work are significant in that they indicate the phase behavior of w/ho emulsions, and that CO2 and CH4 can vary considerably depending on the composition of oil and water content in the system. IFT, relative volume, and PVT measurements provide key information needed to build an adequate fluid model to reduce the uncertainties in reservoir simulation and history matching.
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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 | 0,001 |
| 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 | 0,001 |
| 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 ».