Centralized Gis Digital Platform for High Efficiency Maintenance, Risk Control and Mitigation of Operated Assets.
Bibliographic record
Abstract
Summary Efficiently managing people and resources of large oil and gas assets, can be a complicated task. Many vehicles, people, equipment, and a large amount of data is involved in the development of a field. Moreover, safety is always the first priority and the risk of accidents of different nature and magnitude must always be considered. Greater control of people and vehicles is needed to increase efficiency in the daily operations. We developed an in-house, low-cost digital platform using GIS to increase the situational awareness of the developing field, allowing to handle incidences in a faster and easier way. We were able to stream real-time data from our facilities in Chauvin, Edson, Eagle Ford and Marcellus fields in Canada and US to our Integrated Operations Centres (IOCs), track down real-time position of our maintenance people, remotely identify incidence and quickly dispatch people via a mobile phone application. By developing these real-time datasets, we were able to build web applications such as pipeline network analysis application, an emergency response application, mobile Widgets and various asset dashboards indicating the performance of that asset trough selected KPIs. Using real-time streaming data in our platform increased the operational efficiency and reduced well time down time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".