Technology Focus: Knowledge Management and Training (October 2011)
Notice bibliographique
Résumé
Technology Focus Past articles for this feature have touched on the topic of decision making. Indeed, last year’s article tackled the approach to decision making with a focus on situational awareness. This year, we focus on the availability of knowledge as an aid (on the one hand) and a prerequisite (on the other) to our ability to execute decisions rapidly and correctly. Distilling the key paper topics offered up for this year’s Knowledge Management and Training feature resulted in an interesting “triple-S” configuration of systems, state, and studies. Systems. Knowledge-base (KB) systems are becoming a key component in the workflow for delivering critical decision making. The industry has transcended what may now be considered standard databases. A variety of database configurations is available commercially, with great advances made over recent years. Overlaying available data with information and carefully collated knowledge is an emerging practice. Delivering the KB within a spatial frame of reference, commonly a geographical-information system (GIS), provides for a rich degree of multidimensionality. State. Continuous access to the “state” of the integrated asset, including the reservoir, production, and pipeline systems, is readily available by use of now-commonplace supervisory control and data-acquisition systems. However, the ability to translate the state into meaningful information requires transfer through a series of process layers that filter and aggregate the data such that action may be taken as necessary and appropriate to maintain production targets. Studies. The industry invests significant capital in conducting detailed integrated studies of assets at various points in their life cycles. While keeping internal and external consulting organizations in business, these studies usually deliver an enhancement, depending on the degree of acceptance of the recommendations. Additionally, depending on the organization, the shelf life of these studies may be somewhat limited. Any approach is, therefore, welcomed if an integrated study is incorporated within an enterprise decision-making frame of reference. And the learning process continues…. Knowledge Management and Training additional reading available at OnePetro: www.onepetro.org SPE 145080 • “Reservoir Engineering for Unconventional Gas Reservoirs: What Do We Have To Consider?” by C.R. Clarkson, University of Calgary, et al. SPE 144321 • “Integrating All Available Data To Improve Production in the Marcellus Shale” by Efe Ejofodomi, Schlumberger, et al. OTC 21491 • “GIS Development for Geophysical- and Geotechnical-Data Integration: Application to West Africa Geohazard Assessment” by G. Dan-Unterseh, Fugro France SAS, et al. OTC 21538 • “GIS Technology Development for Sediment Characterization of Angolan Deepwater Soil Conditions” by M. Hamon, Angolan Deepwater Consortium (Doris Engineering), 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 | 0,000 |
| 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 | 0,000 |
| 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 ».