Technology Focus: Reservoir Performance and Modeling (September 2011)
Notice bibliographique
Résumé
Technology Focus The Future Is Already Here, Today! What are the future trends in reservoir management and monitoring, and how will new technology and new ways of communicating change the way we manage our reservoirs? Over the past couple of decades, we have seen significant progress in surveillance, data gathering, and computing capabilities. What are the biggest challenges and opportunities going forward? As the light-oil component of global production decreases, increasing the emphasis on heavy-oil, oil-sand, and natural-gas production will pose new challenges. The number of sensors is increasing, the amount of data is increasing, real-time data are common, and automated analysis is becoming commonplace. How do reservoir-management practices change to accommodate this trend? As technology advances and as nanotechnology enters the stage, our ability to translate data into information and our ability to make decisions on the basis of this information may improve. How will automation and closed-loop reservoir management influence our risk-handling and management decisions? These were among the questions discussed at the SPE Reservoir Surveillance and Data Acquisition 2020 Forum in Santa Fe, New Mexico, May 2011. Automated adjustment of chokes and automated choking of zones are possible today. To use this automation fully, we need to trust the data flow, the analysis of the information, and that the correct action is implemented. Safety concerns are another key aspect to consider. Regardless of the challenges, automation is an emerging trend. Spatial gathering of data also is getting increasingly more focused. This gathering could be crosswell information, data along the well path, or a collection of time-lapse (4D) or electromagnetic data. Integration of different information and data is key to future success. Geological and reservoir models still are the most common way of integration, but generation of data-driven models honoring the physics is one of the new trends. Integration is mostly about the integration of people, making all data available to everybody at the same time and in a format that can be understood by everyone. The selected papers are excellent examples of emerging trends and represent the shape of things to come in reservoir management and performance monitoring. Reservoir Performance and Monitoring additional reading available at OnePetro: www.onepetro.org SPE 131370 • “Using Downhole-Temperature Measurements To Assist Reservoir Characterization and Optimization” by Zhuoyi Li, SPE, Texas A&M University, et al. SPE 134313 • “Large-Scale Laboratory Testing of Petroleum-Reservoir Processes” by David P. Yale, ExxonMobil, et al. SPE 136378 • “Selection of Decision Variables for Large-Scale Production-Optimization Problems Applied to Brugge Field” by Masoud Asadollahi, IRIS/NTNU, et al. SPE 137750 • “Unconventional Imaging for Unconventional Reservoirs” by C.J. Leskiw, University of Calgary, et al.
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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 | 0,000 | 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 ».