Bibliographic record
Abstract
Last summer, while being moved from one well pad to the next, a rig in the Delaware Basin of Texas was updated with some new software. It took 11 hours to complete, and after the rig was powered back up, it went on to drill the vertical section of a horizontal well almost 3 days ahead of schedule. Aside from boasting an impressive stat line, that well represents an important milestone for National Oilwell Varco (NOV) because it is the first to be drilled using the company’s closed-loop automated drilling system in conjunction with a recently launched rig operating system, which the company technically refers to as a process automation system. NOV is telling customers that this technology not only lowers the cost of field development, but delivers higher quality and straighter wellbores through its consistent performance. That automated program in Texas has concluded, but the combined technology package is now being used to drill shale wells on four rigs in the US and one in Canada. There are 16 separate orders for the new process system. These contracts for NOV follow more than 5 years spent using the hardware kit, which includes wired-pipe and a weight-on-bit controller, to drill through more than 2.5 million ft of conventional and unconventional formations. Going forward, the oilfield technology developer is offering a more complete product: automation controlled by highly capable software. This integration means that the digitally connected surface and subsurface machines working on an automated rig now have a digital driller to take orders from. “Our mantra is that the rig of the future is here today—just add software,” remarked Tony Pink, the vice president of strategic sales at NOV. Pink has been involved in the automation initiative at NOV since its genesis and coauthored a recently published technical paper (SPE/IADC 184694) detailing the Texas project that it carried out with Calgary-based Precision Drilling and an unnamed oil and gas explorer. He described the tandem of NOV’s automated hardware and software as “a sophisticated autopilot for rigs” that takes many routine tasks out of the driller’s hands—literally. With the process system in control, drillers can lay off the joystick, stop pressing buttons, and also quit staring at screens in order to maintain their drilling direction.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.111 | 0.076 |
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".