Drilling Systems Automation - Preparing for the Big Jump Forward
Bibliographic record
Abstract
Abstract This paper presents results and knowledge shared from the SPE Applied Technology Workshop held in Vail, Colorado USA in July 2012 titled Well Construction Automation – Preparing for the Big Jump Forward. Automation in drilling and completion operations is coming quickly, and its rapid adoption will leave many industry players behind if they are not aware of the future it will bring. Advances in control and automation of the whole drilling and completion process will increase improvements in safety, performance, quality, reliability, consistency and interoperability. This progressive application of automation will also create shifts in skills and competencies, and transform the role of the driller, rig crew, and service specialists along the way. Advances in automation are being made on multiple fronts, and many lessons are available from its adoption in other industries and the transformation industrial automation afforded in the 1990s. This workshop included important lessons learned from other industries and provided an update on the latest advances in automation developments. It explored the applications of such technologies as robotics, machine learning, and autonomous task performance without continuous human guidance, along with the speed with which these technologies can be applied. This was the first workshop that has actively brought people involved in automation from other industries into the discussion on drilling systems automation. The workshop involved key speakers and participants from leading edge applications including academia and the Defense Advanced Research Projects Agency (DARPA). These organizations are automating things that the drilling industry has not yet heard about. The workshop participants developed plans for adopting these technologies into drilling systems and created a vision of rapid automation adoption into drilling operations.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".