Notice bibliographique
Résumé
Special Section: The Value and Future of Petroleum Engineering The need to understand the future trends of the oil industry has never been greater than it is today. Throughout the history of the oil industry, technology and innovation have made a significant contribution by pushing the boundaries to enable a continuous expansion in production, increasing reserves, and capital efficiency. In the years to come, with the world’s conventional reserves declining, energy companies will inevitably have to move into more challenging and remote locations to explore and produce hydrocarbons. Therefore, the role of innovation and, more specifically, data-science-derived technologies will likely become the key to shaping the future of the oil and gas sector. In fact, there are ample opportunities for oil and gas companies to use Big Data to get more oil and gas out of hydrocarbon reservoirs, reduce capital and operational expenses, increase the speed and accuracy of investment decisions, and improve health and safety while mitigating environmental risks. It is worth mentioning that although the so-called “disruption mandate” faced in every industry, including oil and gas, is not new, its current speed and complexity will foster an adaptive mindset while maintaining its business practices—the key not only for success but also for survival in the age of the digital transformation. Technological advances, such as increased use of Web-based platforms and cutting-edge data-acquisition technologies such as sensors, have made it possible to generate a staggering amount of data in the industry—the aforementioned Big Data—often which is not used efficiently or effectively. One of the key enablers of the data-science-driven technologies for the industry is its ability to convert Big Data into “smart” data. New technologies such as deep learning, cognitive computing, and augmented and virtual reality in general provide a set of tools and techniques to integrate various types of data, quantify uncertainties, identify hidden patterns, and extract useful information. This information is used to predict future trends, foresee behaviors, and answer questions which are often difficult or even impossible to answer through conventional models. Automation, which is derived from Big Data analytics, is a huge step in the direction of improving data science in the immediate future. This evolution holds added benefits such as improving operational efficiency, reducing operational costs, increasing speed, and enhancing self-service modules. The need to automate business processes with the goal of improving functionality and increasing efficiency will be the main driver for the increased adoption of data sciences in the industry. A significant potential for automation exists because it can serve as an ideal aid to daily operations. Many areas where automation can make an immediate and lasting difference for the oil and gas sector include identifying new well targets, improving drilling efficiency, optimizing artificial-lift systems, and monitoring onshore and offshore pipelines and other relevant facilities.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».