Notice bibliographique
Résumé
Technology Focus Despite the recent downturn, a significant number of pilot- or demonstration-scale applications of existing technologies to develop new heavy-oil fields or new technologies to develop existing fields have been reported over the past year. I have selected two of them (SPE 184974 and SPE 184154) in this issue. While they might be projects that started a few years ago when oil prices were at a peak level, they are interesting and still relevant because the projects have continued and valuable results and observations have been shared. Reported cases of cost-effective applications such as well stimulation using chemicals and solvents have also been prominent over the past few years. Well-based applications such as stimulation or production optimization (SPE 184094) are quite useful to improve the productivity in smaller fields. On the other hand, efforts on cost reduction in large-scale thermal projects were obvious. Yet, in laboratory-scale experimental investigations, observations on the use of nanoparticles (SPE 184117) and new- generation chemicals to improve the efficiency of large-scale thermal and non-thermal (mainly chemical flooding) applications are very promising. I also would like to mention high-tech imaging applications to map the heat distribution in field-scale applications (SPE 184971). The areas listed seem to be the trend of new research studies and field applications along with optimization attempts on the basis of data-driven modeling. The focus will also be on new technology attempts toward the reduction of the cost of heavy-oil production, including lower-cost (solar panels) and in-situ steam generation (SPE 184118), minimizing steam needs by use of chemical additives and solvents (solvent-aided thermal—steam or electromagnetic—processes), and nonthermal applications (well stimulation by chemicals and solvents). I hope to read more papers in the coming years on the philosophical approaches to describing the problems and limitations of existing solutions because complex heavy-oil applications still need more effort on model development and experimental data generation. I included SPE 185633 in this issue as a good example of this kind of attempt. Recommended additional reading at OnePetro: www.onepetro.org. SPE 184118 In-Situ Steam Generation: A New Technology Application for Heavy-Oil Production by Ayman R. Al-Nakhli, Saudi Aramco, et al. SPE 184117 Experimental Study for Enhancing Heavy-Oil Recovery by Nanofluid Followed by Steam Flooding NFSF by Osamah Alomair, Kuwait University, et al. SPE 184094 Fluidic-Diode Autonomous Inflow-Control Device for Heavy-Oil Application by Georgina Corona, Halliburton, et al. SPE 184971 Satellite Monitoring of Cyclic Steam Stimulation Without Corner Reflectors by Michael D. Henschel, MDA, 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 ».