Notice bibliographique
Résumé
Technology Focus With continued favorable oil prices, it has been another busy year in terms of heavy-oil discoveries and field developments throughout the world. Both onshore and offshore, many of these new projects are of major scale, requiring significant investment and ranking high in terms of complexity and challenge from a development and recovery standpoint. Many of the commonly used heavy-oil recovery processes are known to introduce substantial physical or chemical changes to the reservoir—some changes produce desirable outcomes, while others can cause highly undesirable effects. This applies generally to both primary- (cold) and enhanced-recovery operations, especially those involving some form of thermal recovery (e.g., various steam-injection methods, in-situ combustion, and electrical heating). The desirable or positive changes may include significant viscosity reduction of the heavy oil or bitumen through thermal or solvent effects; substantial permeability and porosity increases from formation and fluid thermal-expansion effects (e.g., shear dilation), mineral dissolution (e.g., in the case of water or steam injection into carbonate formations), or dendritic wormhole system development in cold-heavy-oil-production-with-sand recovery operations; and favorable changes in wettability (primarily carbonate formations). Understanding and quantifying these positive effects are often crucial to the successful exploitation of heavy-oil reservoirs. Undesirable changes may include significant permeability reduction or plugging (e.g., from asphaltene precipitation, scaling, mineral deposition, shale swelling, or fines migration); large reservoir and overburden deformations and in-situ-stress changes that can lead to well impairments, integrity loss, or caprock failures; and significant CO2 and H2S generation from aquathermolysis, which increases safety and environmental concerns. Two of the papers selected for this feature address physical and chemical effects of steam injection into carbonate and sandstone reservoirs, respectively. The third presents an example of the sophistication inherent to many recent heavy-oil developments in terms of the use of advanced simulation techniques, complex well designs and architectures, and state-of-the-art recovery operation monitoring and control systems. The additional-reading papers reflect the diverse nature of the heavy-oil developments being pursued around the world. They also highlight a few of the numerous innovations and ongoing technology advancements that are helping the industry continually improve recovery performance, lessen environmental effects, and enhance the economic attractiveness of these developments. Recommended additional reading at OnePetro: www.onepetro.org. SPE 157918 SAGD Startup: Leaving the Heat in the Reservoir by M.T.I. Anderson, Suncor Energy, et al. SPE 157865 A Quarter-Century of Progress in the Application of CO2 Immiscible EOR Project in Bati Raman Heavy-Oil Field in Turkey by Secaeddin Sahin, Turkish Petroleum, et al. SPE 159437 An Approach To Model Cold Heavy-Oil Production With Sand (CHOPS) and Post-CHOPS Applications by A. Rangriz Shokri, University of Alberta, et al. SPE 154627 Enhanced Computer-Assisted Model Calibration of Mukhaizna Heavy-Oil Field by Taruna Pillai, Occidental Petroleum, et al.
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 | 0,002 | 0,001 |
| 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 | 0,001 |
| 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 ».