Building the National Workforce for a Sustainable Energy Future
Notice bibliographique
Résumé
Abstract Developing the next generation of industry professionals from a national workforce is a critical requirement to ensure a competitive and sustainable energy industry going into the future. This paper will present a case study on the collaboration between a contractor and an operator to develop a certified training program addressing the needs of the project to ensure long-term sustainability through the upskilling of the local workforce while meeting comprehensive regulatory requirements. In response to changing demographics and local workforces, in addition to the specific needs and challenges faced by the industry, the development of the next generation of professionals requires a training program that addresses a more holistic approach to learning needs focusing on competency minded assessments through a blended learning platform; classroom coaching, e-learning, and on-the- job training including intensive simulated plant training. In addition, through the progression of both augmented/virtual reality software tools, there is even more potential to run real-life scenarios for plant operations and maintenance activities, simulations, plus many more, without exposing trainees to avoidable health and safety risks. These training programs, when certified by independent bodies such as Offshore Petroleum Industry Training Organisation (OPITO), City and Guilds, or Scottish Qualifications Authority (SQA), ensure a robust and inclusive training program that develops professionals who can meet the requirements of future employers. Training programs—when aligned with the end user—can be tailored to either meet specific demand requirements for an increased workforce for operations and maintenance of a new oil and gas facility or address a skill shortage within the end user or country. Petroleum Development Oman (PDO), a national oil company based in Oman, in collaboration with SNC-Lavalin, an engineering and construction service provider, developed a tailored training program to address a skill shortage in commissioning capabilities across the national workforce. The program has an initial six-month classroom coaching phase, coupled with e-learning modules, that progresses into skill-based training within simulation plants. The trainees then travel to the remote working sites for a 30-month program of on-the-job training and competency-based assessment. While on-site, the trainees are embedded into commissioning teams to support with activities while being coached and developed by skilled commissioning specialists to impart critical industry experience and knowledge. The program also had the added value of supporting In-country Value (ICV) initiatives and will ensure PDO has their own in-house local workforce capable of taking on the most complex of commissioning programs for years to come. This strategy establishes a sustainable and long-term approach to ensure the future skills required for the end users continue and operations and maintenance of the plants/facilities within the oil and gas industry are uninterrupted.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,003 |
| 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 | 0,005 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,011 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,010 |
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 source (Gemma direct ou Codex distillé), 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 ».