Experimental Analysis of Heavy-Oil Recovery by Alternate Injection of Steam and Solvent (Hydrocarbon/CO2) in Unconsolidated Sand Reservoirs
Notice bibliographique
Résumé
Summary This work examines the steam-over-solvent injection in fractured reservoirs (SOS-FR) method--which was originally suggested for heavy-oil/bitumen recovery from oil-wet fractured reservoirs—for unconsolidated sands containing heavy oil. One possible application of the method in such media is the post-cold-heavy-oil-production- with-sands (post-CHOPS) enhanced oil recovery (EOR) considering that the wormholes represent the high-permeability (“fractures”) medium while the unproduced original part of the reservoir is the matrix containing the essential portion of the oil. The methodology used in the experiments and the observations will also provide an insight into the applicability of this method in “homogeneous” oil sands producing under gravity drainage during the alternate injection of solvent and steam (or hot water). The SOS-FR method was initially proposed and tested using high-carbon-number (heptane and above) hydrocarbons for solvent at ambient pressure and temperatures lower than 90ºC for oil-wet consolidated sandstones and carbonates (Al-Bahlani and Babadagli 2008, 2009a; Naderi et al. 2013). Later, lower-carbon number (propane and butane) hydrocarbons were tested at higher pressures and temperatures for consolidated water-wet and oilwet sandstones and carbonates (Naderi and Babadagli 2012a). This study focuses on the unconsolidated sands (water-wet nature) and tests three different types of gases as solvent [propane, butane, and carbon dioxide (CO2)] at high-pressure and-temperature conditions (compatible with a typical heavy-oil-sand deposit in Canada). The use of CO2 as a solvent in this type of process was considered as a lower-cost alternative to more-expensive hydrocarbons and for its permanent-storage potential. Several issues are highly critical in this process. Like other hydrocarbon solvents used under nonisothermal conditions, the recovery process is highly sensitive to pressure and temperature because they determine the miscibility level. Also important is the capability of CO2 to extract matrix oil. Our earlier studies with light oil showed that heavier ends can be extracted if enough time is allowed for CO2 to interact with matrix oil (Trivedi and Babadagli 2009a, 2010). The same needs to be investigated for heavy oils. Another challenge is the inverse proportionality of CO2 solubility with temperature. Steam (or heating) is required to condition oil and decrease its viscosity before CO2 injection, but the temperature should be adjusted critically in order not to sacrifice the CO2 solubility of the oil. To clarify all these points and to determine the optimal application conditions (the duration of each cycle and the CO2 soaking time), we conducted a series of experiments by first soaking core samples saturated with heavy oil in steam, followed by CO2. In the third cycle, steam (or hot water) was injected again to produce upgraded oil in the matrix. The experiments were performed under static conditions (soaking sandpack samples in steam or CO2 chambers) at different temperatures and pressures to determine the optimal application conditions. A similar experimental protocol was followed using propane and butane, and the recovery potential of these hydrocarbon solvents was compared with that of CO2.
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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 ».