Directional Advisor Driven Rig and Directional Operation Integration
Bibliographic record
Abstract
Abstract In the North America Land market, drilling operations are typically conducted by a selection of different service companies. Each is responsible for a different aspect of the well construction process, and they must work together to deliver a successful well for the operator. This operational model can lead to overlaps and gaps across the various providers in terms of responsibilities and tasks. Due to differing skill sets across rig and directional personnel, integrating rig and directional execution has practically translated into drilling contractors establishing a directional company and providing both services. While this provides a single accountability point for both, it does not fundamentally change the operational model. A new approach to integrated operations is now possible with the assistance of directional advisor software, coupled with changes in rig driller capability and accountability. This study demonstrated that by utilizing directional advisor software directional operations could be integrated with rig operations without negatively impacting well construction performance. Through the course of a fast-paced, twenty-two (22) well program in western Canada, directional operations were transitioned through a series of steps from a traditional model of two rigsite directional drillers and two rigsite measurement-while-drilling (MWD) engineers to an integrated and reduced crew model where all directional operations were directed from a remote operations center and directional steering was executed by the rig crew.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".