Acidic Steam Modeling and Simulation for Heavy Oil and Extra Heavy Oil Reservoirs
Notice bibliographique
Résumé
Introduction Over the past three decades, the decline in reserves of conventional crude oil has led to the development of several methods in order to enhance oil recovery for heavy oil deposits. Globally, heavy oil accounts for approximately 50% of hydrocarbon volume in place (Ehlig-Economides et al., 2000). There exist sixteen major oil sands deposits all over the world. As a matter of fact, the two largest are the Athabasca oil sands in Northern Alberta and the Orionco - River deposit in Venezuela. By comparison, the Athabasca oil sands alone cover an area of more than 42000 km2, in which oil storage is more than all the known reserves in Saudi Arabia. It is found that only one sixth of over 1.7 trillion barrels of heavy oil are recoverable with current technologies. These technologies include mining, thermal recovery, cold production and etc. Mining only makes economic and engineering sense when the depth of overburden is less than about 75 meters. Hence, only about 10 - 20% of the oil sands can be mined. As a result, recovery of the remaining 80 - 90% of the oil sands depends on the so-called thermal-recovery process, which basically depends on using energy to produce energy. In order to begin to separate the heavy oil from the sand/carbonates, deposits have to be heated to lower the viscosity of the heavy oil. One of these thermal recovery methods is the Solvent Assisted Process (SAP) that appears tremendously successful, especially for bitumen. SAP process involves injection of solvent and steam in several wells. Even though the injector well and producer can be very close, the mechanism of SAP causes a growing steam saturated zone, known as the steam chamber, to expand gradually and eventually allow drainage from a very large volume. Both field and numerical simulation studies have demonstrated the success of SAP drainage. The prediction of SAP performance by numerical simulation is an integral component in the design and management of a SAP project. In this regard, the solvent is chosen to be Acid in this study. In order to inject steam with solvent (acid), two separate chambers of acid and steam are heated up to a certain degree on the surface. Steam only is injected into the well. The well is shut in for 2 hrs and then put on production in which acid is mixed with steam and injected together after cleanup period. Conventional reservoir modeling approach computes multiphase flow in porous media but generally does not take the geomechanical effects into account. Unfortunately, this assumption is not valid for oil sands, because of their high sensitivity on pore pressure and temperature variations but can be applied in carbonate formations.
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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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».