Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».