Bibliographic record
Abstract
Seabed Rig is a small company named after its audacious vision: creating a device able to drill a well on the bottom of the ocean. The company, 20% owned by Statoil, is using what it learned from a prototype to build a rig capable of drilling on land, or an offshore platform, with no workers on site. “You command it, you don’t control it,” said Kenneth Søndervik, vice president of sales and marketing at Seabed. Rather than a person controlling machines putting together pipes, an automated system will respond to a command, such as “pick up 3,000 meters of pipe,” from the computer program controlling drilling. The ability of these machines to work together on their own is essential for Seabed because Statoil needs a rig capable of drilling in the Arctic, and other environments that would put workers in harm’s way. This semantic distinction points to a fundamental change in the drilling business, and the people who work on the rigs. Seabed is building a rig like no other, using components supplied by oil and automation companies. “The big thing is we are not an inventing company. We are an engineering company taking all that’s out there,” said Søndervik. Seabed is working on creating a confined rig floor—the footprint is 9 m by 9 m—with robots programmed using software developed for NASA by Energid Technologies. The US company’s software is also being used to control the next generation of lunar rovers. For the drilling, Seabed will be choosing from a growing number of major oil and service companies developing software that does the job. Statoil, ExxonMobil, Petrobras, Schlumberger, National Oilwell Varco (NOV), and Baker Hughes, represent a sample of the technology leaders seeking ways to program all or parts of the drilling process. Shell appears to have taken it the furthest, with an automated program that has drilled multilateral wells. “It is not science fiction, it is what we have done,” said Peter Sharpe, executive vice president of wells at Shell. Its SCADAdrill System has been demonstrated in Canada and the Netherlands, with testing in progress in two US shale plays, the Marcellus and Haynesville.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".