Industry Initiative – Testing, Certification & Training of Drilling Supervisors to Improve Safety and Performance
Bibliographic record
Abstract
Abstract This paper outlines a new industry initiative and system supported by a group of Operators to reduce NPT and enhance safety performance. It addresses competency of Well-Site Supervisors with a group of Operators aspiring to create industry-wide certification. Its paradigm is Well Control Certification which it will complement, addressing all other areas within both drilling and completions. Competence and experience validation requires a systematic, reliable and repeatable approach. The paper will outline the challenges and response of an Operator group to Well-Site Supervisor Testing, Certification and Training. The Vision is to set the hallmark for quality Well-Site Supervisors globally by providing regularly updated certification. The objective is to: Prevent repetitive mistakes that cost the industry $billions each yearDifferentiate reliable people and support those that need assistanceMeasure reactions to simulations of events that occurred by presenting them as they unfold (to avoid hindsight engineering)Provide tools to improve and measure the individual's developmentShare the resource of quality graded Well-Site SupervisorsIncorporate ‘Human Factors’ to test interaction The downturn represents a unique opportunity to set a new standard for an upturn when there will be shortages of quality personnel. Operators will be able to identify qualified personnel and train those that don't yet meet requirements, providing more quality personnel to the industry.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".