Technology Focus: Reservoir Performance and Monitoring (September 2017)
Notice bibliographique
Résumé
Technology Focus Since the last Reservoir Performance and Monitoring feature in September 2016, the industry trends of significantly improving efficiency and reducing operational costs have continued to be implemented. For instance, at the time of writing, in North America, the US oil rig count has risen impressively for 23 straight weeks and the big players have greatly reduced their exposure to Canada’s oil sands. However, while many efforts are focusing on the optimization of current technologies and the study of past reservoir performance to improve future developments, with fewer capital resources and personnel available, these efforts may yield only incremental improvements. Technology and innovation are seen industrywide as critical to long-term radical efficiency and productivity. Once the industry becomes less concerned about cost savings and more about investing in future technologies and long-term performance, the nonrisk-averse innovation culture from other industries could help us develop new disruptive technologies and implement them in the field. For instance, with the proper resources in place, automated reservoir-performance modeling and monitoring may no longer be a science-fiction scenario. Once this downturn appears in the rear-view mirror, our industry will need to change its model disruptively to thrive sustainably in the next growth cycle. During the past 12 months, 160 technical papers were presented at various conferences and meetings with reservoir-performance-and-monitoring programs and were reviewed for this feature, displaying further advances in reservoir-performance monitoring, analysis, and optimization. The papers selected and recommended as additional reading are representative samples of the reviewed papers. They are a geographically diverse mix of academic work, industrial research and development, and field applications, describing numerical simulation and laboratory research, field-data-acquisition and -interpretation studies, new-technology development and field trials, and multi-year reviews of current technologies and work flows. Recommended additional reading at OnePetro: www.onepetro.org. SPE 181550 Current State and Future Trends in the Use of Downhole Fluid Analysis for Improved Reservoir Evaluation by H. Elshahawi, Shell, et al. SPE 184131 Production Optimization Through Voidage Replacement Using Triggers for Production Rate by Cenk Temizel, Aera Energy, et al. SPE 183195 Development of Crosswell Electromagnetic Monitoring System Using the HTS-SQUID Magnetometer by Makoto Harada, Japan Oil, Gas, and Metals National Corporation, 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 | 0,001 | 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 | 0,001 |
| 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 ».