Removing Risk and Cost from Remote Operations through Intelligent Practices
Bibliographic record
Abstract
Abstract The Arctic is the latest O&G frontier, opening new fields in North West and North Central Russia, Norway, Greenland, Alaska and Canada. The locations imply cold, remote, dark and harsh environments that bring about new challenges to operate in a safe environment while optimizing production and productivity. Furthermore, staffing these assets can provide additional challenges, with increased costs associated with transportation, personnel safety, and often-times high turnover rates. Vast improvements in operations are possible by adding intelligence locally in the producing asset and linking the asset into an enterprise-wide collaborative work environment. This has the effect of greatly reducing the personnel and systems footprint on remote platforms, wellheads and subsea; thus removing risk and cost from operations while improving reliability in such environments. Furthermore, these environments enable institutionalization of best practices between assets, with the goal of true knowledge management We will examine the latest advances in Remote Operations and Remote Collaboration infrastructure and tools and examine the benefits that they bring to operating companies. We will also articulate how these concepts can be incorporated into the project design cycle to improve delivery and how this can influence the supplier / operator relationship during the commissioning phase, but also over the entire life cycle of the asset.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".