Rotary Steerable System Technology Case Studies in the Canadian Foothills: A Challenging Drilling Environment
Bibliographic record
Abstract
The Canadian foothills are situated in a challenging drilling environment with deep wells in hard, abrasive formations that lead to extended drilling times. There is a real concern of casing wear in the upper sections and special care must be taken to ensure the integrity of the casing throughout the life of the well. Studies have shown that even slight doglegs in this vertical section lead to localized “hot spots” where erosion of the casing is focused. Keeping the well straight in this highly dipping formation has been a priority for directional drilling companies and operators. Rotary Steerable Systems (RSSs) have greatly assisted in drilling these wells. From a “closed loop” feature, which automatically seeks a vertical profile in an openhole sidetrack with a carefully controlled dogleg severity (DLS), the rotary steerable tool is proving invaluable in drilling these complex wells and in reducing risks. This paper describes the use of the RSS in different drilling applications and the procedures that have been developed in Western Canada. Case studies illustrate the benefits now being realized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".