Building the National Workforce for a Sustainable Energy Future
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".