Bibliographic record
Abstract
Abstract Advances in autonomous inspection of deepwater subsea facilities are examined to illustrate the favorable enhancement of safety, reliability, reduction in risks, economic benefits and superior data products compared to conventional means. These benefits provide operators with significant improvements over general visual inspection by the addition of sensors that produce 3D models of the structure being inspected. Examples are provided illustrating test data from operations conducted from 2011–2013. Additional benefits include rapid response when a loss of well containment requires large standoff distances between the host vessel and the sensing platform. Three dimensional georegistered models of the entire scene can be rapidly collected within hours of the incident providing responders with a clear vision of the underwater scene along with in-situ status of critical components. Introduction of new sensors support even more advanced capabilities leading to autonomous metrology, hydrocarbon detection tracking and fingerprinting, non-contact corrosion potential measurement, thermal measurements and three dimensional underwater scanning lasers. Application to deepwater life of field inspection will be presented with evidence gained from offshore trials in 2011 and 2012. This emergent technology supports Subsea Facility Inspection Repair and Maintenance, Integrity Management Inspections of Marine Risers, Moorings and anchors, Subsea Pipelines, Flowlines, Umbilicals, and supporting subsea infrastructure. Introduction Frequent risk based assessment of the condition and integrity of subsea equipment is vital to predicting the life of the equipment and prevention of uncontrolled release of hydrocarbons into the environment. Oil and Gas operators must know the state of the equipment that is often thousands of meters below the ocean surface shrouded in the veil of darkness. " Protection of health, safety, and the environment is a critical component of the processes and procedures used to monitor the conditions of offshore surface and subsea facilities and structures" (1). Traditional means of inspecting this equipment employs visual sensors such as video or still cameras mounted on Remotely Operated Vehicles (ROVs) that are hardwired to the operators controlling the vehicle from a ship above the inspection site. Such General Visual Inspection (GVI) requires significant topside support equipment and numerous skilled operators on site to control observe and maintain the ROV and interpret the images along with a large vessel support crew (MTS Journal Article). While the quality of images has improved with the advent of digital High-Definition or HD sensors the images are often degraded by movement of the cameras and the turbidity of the water, reducing the effectiveness of the inspection. In addition, the data provided to clients is often hours upon hours of recorded video that must be archived and revisited by humans for detailed examination.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".