Produced Water Discharge Monitoring
Notice bibliographique
Résumé
Abstract This paper describes how Memorial University is using a new ocean going autonomous underwater vehicle (AUV) to collect ocean environmental monitoring data to take detailed environmental measurements in the water column and to validate ocean environmental monitoring and modeling tools. The research team is developing models to anticipate, manage and mitigate changes in environmental quality, habitat changes and pollutant effects of produced water discharges from offshore oil and gas platforms. Hydrodynamic discharge models that have been developed and validated are described. In the process of executing this research program, highly qualified personnel are being trained in the area of offshore environmental monitoring, risk assessment, and risk management to ensure the sustainability of offshore oil and gas projects. 1. INTRODUCTION Atlantic Canada is in the process of developing offshore oil and gas fields. Within the Province of Newfoundland and Labrador, three offshore fields are already operational and other fields are good prospects. On the Grand Bank, the Hibernia oilfield, 315 km east and south-east of St. John's, has been producing oil since 1997. Average water depth in this area is 80 m and the field is operated using a gravity-based platform. The Terra Nova oilfield is 35 km south-east of Hibernia. It has been producing since 2001. It has a floating platform with average water depth at 95 m. The White Rose oilfield is also close to Hibernia and it has been producing since 2005. The water depth in this area is about 125 m. The Hebron oilfield, which is also close to Hibernia and Terra Nova, may be the next field to be developed. Recent studies estimate a combined recoverable reserve on the Grand Banks of 2.751 billion barrels of oil, up 696 million barrels from previous estimates. In order to predict, manage and mitigate the potential impacts of changes associated with offshore oil and gas development there is a need to develop decision-making tools. The research team in the Faculty of Engineering and Applied Science at Memorial University has been involved in developing environmental decision-making tools to manage waste discharges from offshore oil and gas projects since 1999. The team has conducted several studies on environmental risk assessment and risk management tools for discharge of drilling wastes and produced waters in the marine environment. In the last eight years, the team has focused mainly on the following topics:Development of a probabilistic hydrodynamic model and risk-based design procedure for produced water discharges (Mukhtasor, 2001).Risk-based decision model for drilling waste discharges in the marine environment (Sadiq, 2001).Assessment of performance characteristics of sensors for environmental monitoring using underwater vehicles (Pennell, 2003).Laboratory investigation of the settling characteristics of drilling cuttings (Niu, 2003).Evaluation of various offshore drilling waste treatment technologies using multi-criteria decision-making (Thanyamanta, 2003).Development of decision support software to manage produced water in offshore oilfields (Chowdhury, 2004).Environmental effects monitoring of fish plant effluent in coastal Newfoundland (Adams, 2005)The dispersion of offshore discharged produced waters in the marine environment: hydrodynamic modeling and experimental study (Niu, 2008).
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».