Delivering Consistent Top Quartile Drilling Performance Without Compromise
Bibliographic record
Abstract
Abstract When the operator embarked on an intermittent 5 well exploration, appraisal and development campaign offshore Norway between 2009 and 2012 using the West Alpha there were many challenges. The rig contract was part of a multi company consortium, the well program was varied, a 3rd generation semi submersible rig was being used and all inclusive operating rates were high at around $1,000,000 per day. Following and expanding upon the management principles previously employed 1, the lead operator identified an opportunity to leverage the consortium program and develop a strong relationship with the service companies, in particular the rig contractor. Health, Safety, Security and Environment (HSSE) was a key driver and over 1000 rig days were delivered without a Lost Time Incident (LTI). The lead operator was able to harmonise and standardise procedures used on every well, regardless of who operated the rig. This enabled a much clearer and more consistent message to be delivered to the work environment. This paper will outline the management process taken by the operator and contractor that enabled the following performance to be delivered: 1000 days LTI free rig operations. Record setting drilling performance with improvements up to 40%. No damage to the environment. This paper will also describe how the principles have been transferred to other parts of the operators & contractors organisations.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".