Produced Water - Impact and Analysis Challenges in Cold Regions
Notice bibliographique
Résumé
Abstract Produced water accounts for the largest volume in the production stage of offshore oil and gas operations. There are very few studies dedicated to investigating impacts of produced water discharge in Arctic/cold regions. As exploration expands into these regions, the effects of the cold temperatures, high motion, ice, and extended periods of sunlight on fate and toxicity of constituents will need to be more fully understood due to environmental concerns and production costs. The fate of discharged produced water is determined by dilution and mixing, volatilization/dissolution/sedimentation, and biochemical/chemical reactions. These transport/transformation mechanisms are not well characterized in cold environments. Low temperature and motion may affect efficiency of separation equipment and reduce natural biodegradation and evaporation. As a result, the type of constituents targeted in warmer climates may not be a concern in cold regions and may be replaced by other constituents. The first part of this paper will identify chemicals of concern in produced water for cold regions and model their fate in the environment. Due to the low temperatures, many of the contaminant transformations will be governed by equilibrium. Identification of the chemicals of concern leads into the second theme of this paper. Oil and grease is monitored for regulatory purposes however, what is defined as " oil and grease?? depends on analytical/sampling methods which vary between regions. For instance, some methods measure both the dispersed and the dissolved hydrocarbons, so measurements of dispersed oil tend to be " overestimated?? when compared against limits. Comparison of analytical data between platforms or building of annual trends is also complicated. Discrete sampling, when the sample analysis is done onshore, delays mitigation or corrective actions with respect to process control and does not give an accurate temporal trend in oil in water discharge. Using the information from the " identification and fate?? section of this work we have been developing molecularly imprinted polymers (MIPs) for highly selective isolation, detection and measurement of key constituents (e.g. alkylphenols) and are working toward devices to house the MIPs for online analysis. The development of these systems will allow the non-specialist to quickly identify constituents in the field and enable extensive data collection in real time and hence the knowledge to make informed and timely decisions.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 tête enseignante, 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 ».