Robots for Normally Unattended Facilities
Bibliographic record
Abstract
Abstract Based on economic reasons and safety considerations, Normally Unattended Facilities (NUF) have been proven as a viable option in offshore oil and gas Industry for developing marginal fields. Developments in digitalization and communication are fundamental reasons for this paradigm shift. Digital technologies and robotics have helped operators to considerably lower Person on Board (POB) count on existing attended facilities. This reduced operating expenses (OPEX). Due to minimal operators presence, these facilities require smaller hotel facilities and safety escape devices. These facilities are also designed with walk-to-work as basic maintenance philosophy with supply vessels providing major utilities. This reduces capital expenditures (CAPEX). The associated advantage of these facilities is the reduction in greenhouse gas (GHG) emissions. Due to lower power requirements, most of these new facilities are powered by solar or wind energy. This negates burning fuel gas for power generation. The facilities are designed with one visit per year as basis of operation. Limited supply vessel visits and offshore operators’ trips contributes to GHG reduction. Apart from oil and gas facilities, offshore wind turbines are designed as unattended installations due to limited space availability. Operation and inspection of these installations are carried out remotely. Most of the unattended installations are designed with control from remote locations. Availability of reliable and current data is of paramount importance for remote control. Collecting data from source of truth is a critical activity. Current developments in transducers and smart instrumentation have achieved proven means for operational data collection. However, for maintenance data like deck corrosion, paint condition etc., operators primarily depend upon manual methods. Collection of data required for periodic inspections, like pressure vessel wall thickness or piping erosion, are predominantly manual. Robots can be successfully deployed as data collectors. At present robots are used as aids for operators to carry out risky and dangerous operations. These robots can be either teleported or autonomous robots and require continuous supervision and command. To a limited extent, customized robots are used for data collection in some facilities. This paper highlights some of commercially available robotics applications for offshore installations, discusses cost savings and proposes an integrated approach for robots as data collectors in normally unattended facilities. This paper will also cover some of the aspects to be taken care while designing offshore facility to suit robots’ deployment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".