Probabilistic Well Time Estimation Revisited
Bibliographic record
Abstract
Abstract Probabilistic estimation of well duration has been common practice for over a decade; many papers have been written on the subject, and commercial software is available for the purpose. Is the subject therefore mature? The authors suggest that this is not the case, and show that several essential aspects of both data characterisation and probabilistic analysis have been overlooked in previous contributions. A database of 104 central North Sea wells was independently re-analysed for non-productive time (NPT) from the original daily drilling reports. Mechanical extreme NPT events (those over 2.5 days) were only 4% by number, but contributed 51% of NPT by duration. It is shown that mechanical parent NPT, mechanical extreme NPT, open water WOW, and riser connected WOW are all statistically distinct, with very different occurrence frequencies and probability density functions (PDFs). These data vary widely by well type, installation type, and time of year. The paper gives tables of all required occurrence frequencies and PDF parameters, together with a full theoretical basis. Earlier workers did not publish this information, nor did they observe that the four NPT types above are statistically distinct. It is shown that trouble-free time plus mechanical parent NPT is log-normally distributed, whereas the other PDFs are Weibull. These PDF types are not currently available in commercial software, and the authors are working with a software provider to remedy this deficiency. Example results (PDFs of predicted duration) are given by well type, installation, and time of year. They are in good agreement with the historical database.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".