A New Drillstring Fatigue Supervision System
Bibliographic record
Abstract
Abstract With the development of complex wells, extended reach wells and deep wells (for deeply buried reservoirs production), drillstring elements are subject to several kinds of cyclic stresses. Eventhough such stresses are often below the material endurance threshold, the induced fatigue accumulation over time may generate the failure of drillpipes while drilling. Harsh drilling conditions would ideally require an advanced follow-up of the fatigue of each element of the drillstring. Tracking the mechanical and dynamic history of each drillstring element all over its life -from one site to another- is a challenge. To reach this objective, the team project regrouping experts from IFP, Cybernextix, Vam Drilling and Pride International has developed a new way to monitor the fatigue of each element of the drillstring. The proposed method is based on a RFID (Radio frequency identification) chip (called tag) used in association with a modeling approach to accurately estimate the fatigue damage. Pertinent parameters are estimated by modeling, based the operational context, and are written afterwards on the referenced element tag associated with each element of the drillstring. Reading of the identification number and the updating of the memory with new information about fatigue duration, etc., are automatically performed while the drillstring element goes through the rotary table upwards. This new system and its first rig tests are discussed in this paper.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".