Bibliographic record
Abstract
There is a lot of buzz about digital data and analytics changing drilling, and far less talk about teaching those who drill the wells how to deliver on that promise. That looks like a major oversight to Kevin Krausert, the chief executive officer of Beaver Drilling. “Our industry is trying to figure out how we can engineer the humans out of this change. And we need to be thinking about how we put the humans in charge of engineering this change,” Krausert said. His critique was delivered during a panel discussion at this year’s SPE/IADC Drilling Conference and Exhibition. The head of the small Canadian drilling company argued that the value of digital- and data-driven change will depend on whether workers skilled at running a mechanical system are prepared to lead teams finding way to use digital tools to drill more productively. To try to back up that claim, Beaver created a 2-year program in partnership with the University of Calgary to create the Avatar program to prepare students ranging from roughnecks to drilling managers for digital change. The notion that an employee for a drilling contractor who may never have gone to college is the point person for drilling innovation is counterintuitive. But experience with drilling improvement programs has found that the ones on the rigs play a critical role. “The fundamental shift has to be in the•mindset of the guys who are right there in the thick of it. They are the ones affecting the change; they are the ones who can lead the movement,” said Jennifer Zieglgansberger, an executive coach in Calgary who partnered with Beaver to create Avatar. People-Driven Change Beaver’s story is one of four examples of worker-centered innovation efforts in the oil industry. Occidental Petroleum has slashed drilling costs using a flood of data from wired drilling pipe. The key to doing so was the teamwork on that rig, and others nearby, to systematically improve performance (SPE 194093). Advanced technology provided an unusually detailed picture for a crew “systematically engaging the rig in identifying opportunities for improvement and using engineering design to make continuous improvements that can be used anywhere,” said Molly Giltner, a senior drilling engineering supervisor at Occidental, who delivered a paper on the project at the drilling conference. “People can do it, they just need the information,” said Giltner, the project leader. “They need to be told they can change things. Motivating people makes a huge difference. We do not talk about that a lot as engineers and they do not talk about that in school.” Corva has grown rapidly due to the strong demand for its real-time data and analysis system. In a year, it has gone from a couple of rigs equipped with its drilling advisory system to 250 rigs and 35 clients by September, said Ryan Dawson, chief executive officer of Corva.
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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.018 |
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".