Notice bibliographique
Résumé
There is a lot of buzz about digital data and analytics changing drilling, and far less talk about teaching those who drill the wells how to deliver on that promise. That looks like a major oversight to Kevin Krausert, the chief executive officer of Beaver Drilling. “Our industry is trying to figure out how we can engineer the humans out of this change. And we need to be thinking about how we put the humans in charge of engineering this change,” Krausert said. His critique was delivered during a panel discussion at this year’s SPE/IADC Drilling Conference and Exhibition. The head of the small Canadian drilling company argued that the value of digital- and data-driven change will depend on whether workers skilled at running a mechanical system are prepared to lead teams finding way to use digital tools to drill more productively. To try to back up that claim, Beaver created a 2-year program in partnership with the University of Calgary to create the Avatar program to prepare students ranging from roughnecks to drilling managers for digital change. The notion that an employee for a drilling contractor who may never have gone to college is the point person for drilling innovation is counterintuitive. But experience with drilling improvement programs has found that the ones on the rigs play a critical role. “The fundamental shift has to be in the•mindset of the guys who are right there in the thick of it. They are the ones affecting the change; they are the ones who can lead the movement,” said Jennifer Zieglgansberger, an executive coach in Calgary who partnered with Beaver to create Avatar. People-Driven Change Beaver’s story is one of four examples of worker-centered innovation efforts in the oil industry. Occidental Petroleum has slashed drilling costs using a flood of data from wired drilling pipe. The key to doing so was the teamwork on that rig, and others nearby, to systematically improve performance (SPE 194093). Advanced technology provided an unusually detailed picture for a crew “systematically engaging the rig in identifying opportunities for improvement and using engineering design to make continuous improvements that can be used anywhere,” said Molly Giltner, a senior drilling engineering supervisor at Occidental, who delivered a paper on the project at the drilling conference. “People can do it, they just need the information,” said Giltner, the project leader. “They need to be told they can change things. Motivating people makes a huge difference. We do not talk about that a lot as engineers and they do not talk about that in school.” Corva has grown rapidly due to the strong demand for its real-time data and analysis system. In a year, it has gone from a couple of rigs equipped with its drilling advisory system to 250 rigs and 35 clients by September, said Ryan Dawson, chief executive officer of Corva.
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 | 0,000 | 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 ».