Notice bibliographique
Résumé
Technology Focus Reviewing a myriad of papers presented at different conferences during the past year and attending some of those to hear the oral presentation versions, I can group the current trends in heavy-oil operations and research into two major categories: Process optimization and use of chemicals as additives to steam and water. Here are some highlights from those categories. Several papers focused on purely computational methods—including multi-objective optimization and proxy models—to optimize the steam-assisted gravity drainage (SAGD) method with specific attention on preferential steam allocation. Real-life examples of these applications consist of inflow- and outflow-control devices and steam splitters developed for diverting the steam to preferential zones, thus minimizing the use of steam. As an indirect meth-od to provide supplemental data for the optimized solutions, seismic data were also used. Applications of seismic waves include the depiction of steam-chamber development and monitoring wellbore integrity. Considering that many SAGD operations are reaching maturity, these applications will help maintain the steam/oil ratios at low levels, therefore extending the life of the processes. In another group of studies, chemical additives were tested at the laboratory scale to improve cold (microemulsion formulations) and thermal (to reduce steam/oil ratio) heavy-oil recovery. Examples of newly trialed chemicals include ionic liquids, foam additives, anionic and hydrophobic surfactants, switchable-hydrophilicity tertiary amines, nano-fluids (dispersed SiO2 and ZrO2), ethers, alcohols, and chelating agents as steam additives and microemulsion formulations using SiO2, anionic and nonionic surfactants as well as deep eutectic solvents for cold production. Although most of the listed optimization studies (specific examples reported include the Mackay River, Firebag, and Surmont fields) and chemical tests were done for SAGD conditions (especially at the mature level), these new technologies and applications will be a guidance to the other types of steam operations (cyclic steam injection and steamflooding) using horizontal injectors and producers. In addition to the discussed studies and practices, a plethora of field-scale activities from the Middle East, far east Asia, and the Americas have been reported on consistently. Most of those are early-stage practices or pilots, but a few are related to mature heavy-oil recovery operations. Examples include Kuwait (data collection and development plans for new heavy-oil potentials), Oman (steam management surveillance in the Mukhaizna field), Brazil (development options for the offshore Atalanta field), Venezuela (ongoing operations in the Orinoco belt), Alaska (field-scale polymer-flooding test), China (practices in the Liaohe and Bohai Bay fields), Canada (cold production using methane/ propane mixture and electrical heating pilots in Alberta), Colombia (applications of autonomous inflow-control devices in the Rubiales field), and Indonesia (identification of rod-pump failures in the Duri field). Recommended additional reading at OnePetro: www.onepetro.org. SPE 196072 Laboratory and Field Testing of a Steam-Limiting Flow-Control Device Developed for Thermal Applications by Marty Lastiwka, Suncor Energy, et al. SPE 197321 Surveillance Approach of Steam Management in a Heavy-Oil Reservoir, Oman by Hajer Al Mahruqi, Occidental Petroleum SPE 198697 Effective Reservoir Management With Flow-Control Devices for SAGD Producer Wells in the Mackay River by Giuseppe Rosi, RGL Reservoir Management, 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) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 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 ».