Applications of Autonomous Underwater Vehicles in Offshore Petroleum Industry Environmental Effects Monitoring
Notice bibliographique
Résumé
Abstract Environmental Effects Monitoring (EEM) is an important tool in assisting Environmental Risk Assessment (ERA). EEM in the offshore petroleum industry has been conducted worldwide, but traditional approaches have struggled to keep apace as exploration and production activities move to frontier regions, such as increasingly deeper waters and Arctic regions. This paper proposes the use of autonomous underwater vehicles (AUVs) for environmental monitoring of offshore facilities as a means of improving and expanding the overall monitoring program. The paper provides a review of technical and procedural issues involved in this application of AUV technology, including the current status of offshore oil and gas EEM, a review of available AUVs and a survey of developments in in situ sensors. Introduction Offshore petroleum industry operations affect the marine environment in a variety of ways: high sound levels from seismic surveys that affect marine animals; exposure of marine organisms to drilling mud, produced water discharges and accidentally spilled oils; and the physical alteration of habitat due to the construction of submarine structures. The potential risks to the environment posed by offshore oil and gas operations support the need for effective Environmental Effects Monitoring (EEM) around the project development areas. EEM is a central component of environmental protection and management strategies designed to minimize the consequences of anthropogenic activities(1). It is a very important tool in assisting Environmental Risk Assessment (ERA) which is seen from many studies that link EEM and ERA together(2, 3). EEM is required by regulations governing industry activities offshore, and by government agencies in relation to cumulative impact assessment studies(4). The United States started the use of environmental monitoring programs in 1973. The Mineral Management Services (MMS) is currently responsible for managing oil and gas activities on the outer continental shelf (OCS). In the early stages of EEM programs, MMS monitored the effects of petroleum exploration activities on the George's Bank, Middle Atlantic OCS and the Gulf of Mexico. Early monitoring programs mainly focused on the effects of drilling wastes on benthic communities through a variety of sampling methods, such as camera transects, crab traps, bottom trawls and box corers. The MMS has also monitored the effects of petroleum development and production activities in the Gulf of Mexico, Santa Maria and Western Santa Barbara Channels off California, and in the Alaska Beaufort Sea. Trace metals and hydrocarbons in the water column, sediments, pore waters and biological tissues are collected and analyzed. In Canada, both government agencies and operators have carried out EEM. For example, Petro-Canada collected sediment samples from 49 stations and water samples from 24 stations in an area located in the vicinity of the Terra Nova Oil Field during 2000 to 2001. Analyses of samples included hydrocarbon concentration, metal concentration, particle size and the presence of sulphur, sulphide and ammonia(5). Fisheries and Oceans Canada also conducts annual EEM missions at the Hibernia, Terra Nova and The baud fields off the east coast of Canada. Both sediment and water samples are collected and the biodiversity of benthic organisms are studied using underwater photography.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».