Well Control Course Redesign for the 21st Century
Bibliographic record
Abstract
Abstract Objectives/Scope This paper describes a new approach to the design and delivery of well control training. The modifications and improvements largely address IOGP and industry guidelines. Conventional and newly-required well control training subjects are addressed by applying Andragogy, adult-learning theory. The flipped classroom is described. Methods, Procedures, Process The new generation of drillers and learners expect new learning dynamics and methods, including collaboration with social groups, cloud based material and non-connective based training that is interactive. Students today are changing the way people of this new generation learn. This course material and delivery addresses these learning protocols. The course was developed around adult learning theories and assurance concepts. Results, Observations, Conclusions A Human Factors approach was used todevelop the course. This approach identified the critical technical components needed in a well control event, determined what a supervisor needs to do in a crisis and how they work together in a team. Key leadership elements are assessed. Well control event scenarios are used in the course. The course was designed using a Know – Assess – Decide – Act methodology. Students are led to understand how initial information, unfolding events and how the results of their actions affect the potential success of controlling the well after an influx. Novel/Additive Information Andragogy, learning styles and methods and a flipped classroom are used in the course. The course design does away with the typical pedagogical learning used today and replaces it with adult learning theory. The improvement outcome for this redesigned well control training program is to give confidence that the right decisions will be made at the right time. This assurance is critical for the students, their employers, accreditation institutions and government bodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".