Casing Directional Drilling Using a Rotary Steerable System
Bibliographic record
Abstract
Abstract The hardware used in casing while drilling operations over the past 5 years has proved to be robust and reliable. Several directional wells have been drilled successfully with casing using positive displacement motors (PDMs), but the drilling efficiency was significantly degraded in most of them. This paper documents the drilling of the first two wells to utilize a rotary steerable system (RSS) to improve drilling efficiency when drilling with casing. Combining an RSS with casing while drilling operations seemed to be a natural way to eliminate the major weaknesses in motor bottomhole assembly (BHA) designs. Rotary steerable systems had not previously been used with casing while drilling because both are new technologies focused on different environments. Casing while drilling developed on land operations while rotary steerables have been popular for offshore projects. ConocoPhillips has drilled more than 94 wells in the Lobo trend of South Texas since 2001 using the new technique. As part of a technology demonstration project to accelerate the technology to offshore applications, two wells were drilled with an RSS. The first was an operational test conducted by drilling vertically with the RSS. The second was a full directional test o with a build to 29° directional turn. and then a drop to vertical, including a 100°
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".