Notice bibliographique
Résumé
Special Section: The Value and Future of Petroleum Engineering Technology transfer in the oil and gas industry is often viewed as a one-way, inbound street. It comes with a good reason. The industry, from upstream to downstream, has a well-established track record of seeking out and ingesting external innovation. And thanks to the expanding menu of digital technologies available today—advanced computing programs, the internet-of-things, and robotics—this appetite is growing. One of the most important periods of technology transfer for the oil and gas industry came in the years following World War II. Military technologies without a war to help fight would find new homes within engineering companies tasked with modernizing the upstream sector. Some of the most successful adaptations include shaped charges, offshore structures, and reeled pipelines. But sometimes a new link is added to this chain of innovation. As the technology that comes into the oil and gas business becomes refined, its potential soon trickles into the periphery of another sector. The other side of this outbound transfer includes native-born technologies that are adapted to the benefit of others. In either case, the most well-trod pathways of this technology transfer lead into the areas of Earth and life sciences, space exploration, and renewable energy where exploration and production innovations are enabling new areas of research and understanding. Medical Science Reservoir models are easy to take for granted since they have been a fixture of the modern oil and gas sector for more than a generation. But a government-backed study in Norway aims to see if the technology can be carried over into the medical world to improve how doctors interpret magnetic resonance imaging (MRI), which may save lives. The idea is based on the similarities between the human brain and oil reservoirs. Because both are dual-porosity media, researchers think the industry’s reservoir models could lead to a breakthrough. The $1.1 million project is being led by The International Research Institute of Stavanger, or IRIS, which has more than 20 years of experience in reservoir modeling. The roots of the collaboration began with Pumps and Pipes, an international group of technologists first launched in Houston to explore synergies between the oil and gas and medical sectors. Fiber optics have also been around for decades, and are considered to be the backbone of the world’s communications network. The oil and gas industry has used the technology for an extended period as well to monitor the habits of their oil wells, including how their pressure changes. Quebec-based Opsens is one of the companies that began in this application area but has recently adapted its fiber-optic sensors to be used inside the human body. The medical version of its technology has been approved to take pressure measurements inside arteries that affect heart function. The potentially disruptive procedure it enables allows doctors to quickly assess the severity of blockages and legions to see if they require angioplasty or less invasive therapies.
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 | 0,001 |
| 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 ».