Technology Focus: Reservoir Performance and Monitoring (September 2015)
Notice bibliographique
Résumé
Technology Focus Recent months have been very challenging for the oil and gas industry. When the previous Reservoir Performance and Monitoring feature appeared in JPT in September 2014, Brent was trading at approximately USD 105/bbl. At the end of June 2015, when this statement was written, its price was 41% lower, at just above USD 62/bbl. In addition, the world rig counts reported by Baker Hughes in September 2014 and June 2015 were 3,659 and 2,152, respectively, a decrease of approximately 41% (rig count is a trailing indicator of oil price). The international and North America rigcount decreases between September 2014 and June 2015 were approximately 12 and 57%, respectively. These steep decreases, mostly the direct consequence of a global imbalance between demand and supply, are warnings that our conventional innovation schemes need to be recalibrated. Even if the rig count has been dropping, the rig efficiency continues to improve. From multiwell pads to advanced drilling technologies, innovation is helping keep current production high. If current low prices persist, further innovation may improve the alignment between short-term production and long-term recovery, while lowering overall production costs. Maximizing short-term production and optimizing longterm recovery may provide key opportunities for cost savings during the next downturn of major proportions. This could be achieved through the industry’s ability to innovatively acquire and interpret data for optimizing the reservoir performance. The industry is continuously looking at new monitoring devices and techniques, and there are huge opportunities for monitoring fieldwide data with the ultimate goal of understanding the reservoir better and predicting its short- and long-term production more accurately. The current downturn may be a great opportunity for the big-data revolution from other industries to be extended to the oil and gas industry in general and to reservoir performance in particular. As a result of the industry’s current efforts to improve reservoir performance and reduce production costs, many great papers have been presented at recent SPE conferences and meetings. From the more than 100 papers reviewed for this feature, approximately half of them present case histories, field-data interpretation, and work flows, and the other half present theoretical and laboratory results. The papers summarized in this feature and recommended as additional reading are excellent samples of this distribution. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 170619 Wireless Inflow Monitoring in a Subsea Field Development: A Case Study From the Hyme Field, Offshore Mid-Norway by Svein Mjaaland, Statoil, et al. IPTC 18115 Three-Dimensional Visualization of Solvent Chamber Growth in Solvent-Injection Processes: An Experimental Approach by F. Fang, University of Alberta, et al. SPE 171932 Linking Diagenesis, NMR, and Dynamic Data for Accurate Flow Characterization of Heterogeneous Carbonate Reservoir by Umer Farooq, Abu Dhabi Company for Onshore Oil Operations, et al. SPE 172929 Production Forecast, Analysis, and Simulation of Eagle Ford Shale Oil Wells by Basel Alotaibi, Texas A&M University, 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 | 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 ».