Special Oversight Measures for Deepwater and Critical Wells in Harsh Environments
Bibliographic record
Abstract
Abstract As a result of the Deepwater Horizon disaster and Macondo well blowout, the Canada-Newfoundland & Labrador Offshore Petroleum Board (C-NLOPB) identified the need to establish Special Oversight Measures for deepwater wells. The Special Oversight Measures were implemented in response to the heightened concerns regarding offshore drilling risks, and to have extra visibility of Operator efforts in applying the lessons learned from the incident to prevent similar occurrences in the C-NLOPB jurisdiction. The application of the C-NLOPB's Special Oversight Measures has since evolved to include higher risk drilling programs such as high pressure and high temperature (HPHT) wells, ultra-deepwater wells, and harsh environment drilling where there is increased potential for a well control incident to occur. The C-NLOPB's Special Oversight Measures are an initiative that has significantly increased the rigour with which drilling in extreme offshore conditions is regulated in Eastern Canada. The Canada Nova Scotia Offshore Petroleum Board (CNSOPB) has also adopted these special measures to aid in their oversight of deepwater drilling programs. These initiatives have also been presented to North Sea regulatory working groups to communicate success and advancement in this area. There are a tremendous number of parallels between drilling in the harsh environment of offshore Newfoundland and Labrador and arctic regions worldwide. The C-NLOPB's Special Oversight Measures are being shared with the aim of collaboratively establishing a heightened regulatory expectation on the stringent application of best practices for high risk drilling campaigns in Canada and worldwide.
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 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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".