Notice bibliographique
Résumé
Technology Focus With interest in heavy oil continuing to grow rapidly worldwide, SPE has responded to the need for increased dissemination of technical information related to the exploitation of these unconventional hydrocarbon resources. In one new initiative, SPE joined forces with the Canadian Society for Unconventional Gas to organize the 2010 Canadian Unconventional Resources and International Petroleum Conference in Calgary, in October. SPE also organized heavy-oil advanced-technology workshops and expanded heavy-oil content of regularly scheduled conferences in several Middle East countries, Russia, and China in 2010, signaling the growing importance of heavy-oil reserves to these regions. I often get asked: “What is the best recovery method to use for this heavy-oil reservoir?” The answer can vary substantially, depending on the reservoir setting and fluid properties. Therefore, as with any new field, the process of determining the preferred development strategy begins with a detailed and accurate characterization of the reservoir and fluid properties. However, in the case of heavy oil, unanticipated technical challenges are encountered routinely in accomplishing this basic exercise. For example, while tools and equipment are readily available and proved for capturing live downhole-fluid samples in conventional-oil reservoirs, this is not the case for heavy oils, especially those with in-situ viscosities exceeding several hundred centipoises, let alone thousands or tens of thousands of centipoises. Many heavy-oil reservoirs consist of unconsolidated sand formations, which also makes it difficult to acquire either fluid or undisturbed core samples and then to obtain accurate permeability and porosity data. For thermal projects, determining accurate rock and fluid properties as a function of temperature is important but is not an easy task. Specialized equipment and field-sampling/laboratory-testing techniques along with ample experience typically are required to obtain reliable data. It is also worth noting that the trend over the past few years has been to give much more attention during initial development planning to the sequencing of different enhanced-oil-recovery (EOR) strategies to maximize recovery from heavy-oil reservoirs. On the basis of the many papers written this past year related to polymer flooding of heavy-oil reservoirs, it appears that recent technological advancements and application successes have led to this becoming a viable EOR alternative for a wide range of in-situ fluid viscosities. Finally, the need for conducting pilot operations to establish actual reservoir and well performance and to validate expectations cannot be emphasized enough. Heavy Oil additional reading available at OnePetro: www.onepetro.org SPE 137639 “Thermal Properties of Formations From Core Analysis: Evolution in Measurement Methods, Equipment, and Experimental Data in Relation to Thermal EOR” by Y.A. Popov, Schlumberger, et al. SPE 134849 “In-Situ Heavy-Oil Fluid-Density and -Viscosity Determination Using Wireline Formation Testers in Carbonates Drilled With Water-Based Mud” by Ridvan Akkurt, Saudi Aramco, et al. SPE 136665 “Viscosity of Foamy Oils” by A.B. Alshmakhy, SPE, Weatherford, 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 | 0,003 | 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 ».