Notice bibliographique
Résumé
Technology Focus Creativity and innovation have long characterized production and facilities, and this year is no exception. Much of the work reported this past year was conducted during the recent period of low oil prices. The economic challenges of the oil industry clearly have provided a strong stimulus for even more creativity and innovation. The use of big data and analytics appeared in a number of papers with an emphasis on the use of artificial intelligence (AI) for building databases used to monitor the health of equipment and structure risk-based-inspection (RBI) strategies. Monitoring data inputs from thousands of sensors (paper OTC 28990) allows an AI application to predict an impending failure and notify operators by text or email when the incipient problem is detected so that proactive maintenance can be scheduled to avoid an unplanned shutdown or catastrophic failure. This strategy is being successfully applied to compressors but no doubt will be used to monitor other high-cost, critical service equipment as well (paper SPE 188803). Progress continues on the design and application of inflow-control devices (ICDs). Introduced only a few years ago, these devices are still in a rapid development stage for both design and application. Now, ICDs are applied successfully to improve the fluid-injection patterns for both steamfloods and waterfloods, the latter being described as a successful field application (paper SPE 189824). For steamfloods, passive and autonomous ICD designs were evaluated and their performance modeled using computational fluid dynamics (paper SPE 189721). The integration of subsurface modeling and surface-facility design by the development of a data-driven stochastic work flow (paper SPE 187462) demonstrated a means to reduce both subsurface and facility costs by reducing the biases that inevitably come into play during the generation of a field-development plan. Other innovative work was reported on field optimization, the prediction of asphaltene precipitation, and the integration of RBI with vibration-induced fatigue failure of installed piping systems. Interesting work not discussed here includes evaluating corrosion under severe conditions and the development of an oil-droplet-coalescing pump for use in water treatment. Recommended additional reading at OnePetro: www.onepetro.org. SPE 188803 Machines Performance Algorithmic Modeling for Anticipating Machines Health Using Real-Time Condition-Monitoring Data by W. Almadhoun, ADMA-OPCO, et al. SPE 189721 Evaluation of Inflow-Control-Device Performance Using Computational Fluid Dynamics by M. Miersma, University of Alberta, et al. SPE 187256 Production Optimization of Shenzi Field in the Deepwater Gulf of Mexico by P. Ashton, BHP, et al. SPE 190149 A Diagnostic Approach To Predict Asphaltene Deposition in Reservoir and Wellbore by Davud Davudov, University of Oklahoma, et al. OTC 28352 Integrating an RBI Approach for Vibration-Induced Fatigue Into a Mechanical-Integrity Program by Paul Crowther, Wood, 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 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».