Centralized Gis Digital Platform for High Efficiency Maintenance, Risk Control and Mitigation of Operated Assets.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it