Notice bibliographique
Résumé
Abstract Fast-SAGD, a modification of the SAGD process, makes use of additional single horizontal wells alongside the SAGD well pair to expand the steam chamber laterally. This method uses fewer wells and could reduce cost compared to a SAGD operation requiring paired parallel wells one above the other. In this study, the Fast-SAGD process has been optimized through numerical reservoir simulations for the three typical oil sands areas in Alberta, Athabasca, Cold Lake, and Peace River reservoirs. Two key reservoir parameters, reservoir thickness and permeability, were screened under various operating conditions to characterize the optimal applicable reservoir and operating conditions for the Fast-SAGD process. Economic analysis was then used for optimizing the Fast-SAGD operating conditions. The simulation results indicate improved energy efficiency and productivity in most cases for the Fast-SAGD process; in those cases, the project economics were enhanced compared to the SAGD process. Both Cold Lake- and Peace River-type reservoirs are good candidates for a Fast-SAGD application rather than a conventional SAGD application. In shallow Athabasca-type reservoirs, which are thick with high permeability, Fast-SAGD has shown to be almost as good a candidate as SAGD for optimal recovery. This new process demonstrates improved efficiency and lower costs for extracting heavy oil from these important reservoirs. Introduction The SAGD process has been implemented for the commercial production of oil sands in Alberta. A number of research studies(1)(2)(3) have found that the steam-assisted gravity drainage (SAGD) process is feasible for reservoirs thicker than 20 m with the permeability in excess of 2 Darcy. The Fast-SAGD process works with offset wells operated with cyclic steam stimulation (CSS) beside the SAGD well pair in order to accelerate the growth of the steam chamber sideways (4). This method uses fewer wells and could reduce cost compared to a SAGD operation requiring paired parallel wells one above the other. Previous numerical studies(1)(5) have shown that the Fast- SAGD process would enhance the thermal efficiency in a reservoir, resulting in better production performance as compared to the conventional SAGD process in a typical Cold Lake-type reservoir. In our studies, the Fast-SAGD operating conditions were optimized through numerical reservoir simulations for the three typical oil sands areas in Alberta, shallow Athabasca (AB), Cold Lake (CL), and Peace River (PR) reservoirs. Two key reservoir parameters, reservoir thickness and permeability, were screened under various operating conditions to characterize the optimal applicable reservoir for the Fast-SAGD process in each deposit. A simple thermal efficiency parameter (STEP) was developed on the basis of production performance parameters CSOR, CDOR, and RF. It was validated as an economicindicator for optimizing SAGD performance(6)(7). This same economic indicator will also be used in this study to optimize the Fast-SAGD operating conditions. Optimizing the Fast-SAGD Process The Fast-SAGD process was introduced by Polikar et al. (4), combining the SAGD and CSS processes. The CSS helps the steam chamber formed by SAGD propagate sideways.
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 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 | 0,001 | 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 ».