Bypass Pigging Operation Experience and Flow Assurance Study
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Notice bibliographique
Résumé
Abstract Pigging has been employed over the decades to provide liquid, solid and depositmanagement in pipelines. Pigging of subsea tie-backs to manage liquid holdupand deposits is a significant flow assurance challenge. Deepwater developmentscontinue to increase this challenge, with longer flowlines, longer risers andgreater potential for solids deposition. Significant production risks include aseparator trip caused by a surge in liquids/solids into topsides equipment, andthe potential for lost production due to a stuck pig in the offshore flowline. By-pass pigging has been widely used to reduce the pigging risks in longflowlines by spreading the collected liquid and/or scraped deposits in front ofthe pig. By-pass pigging is a complex fluid dynamics operation. Only limitedsuccess in predicting slug size and duration has been reported for by-passpigging operations, even with sophisticated modeling tools. This paper presentsinformation about by-pass pigging experience for a wet gas/condensate subseaflowline (ID = 20.6" and L = ~10 km) running from a wellhead platform to acentral processing platform. The operational experience includes optimizationof the by-pass opening in the pig, separator liquid drain rate control, andseparator level control. Solids recovery will be discussed. Field data fromby-pass pigging operations will be compared to predictions from two models. Based on the field data, updated recommendations for modeling by-pass piggingoperations will be discussed. Introduction Pigging has been used for many decades to perform various tasks such aspipeline inspection and cleaning. Numerous pipeline systems rely on pigging asa major flow assurance control for hydraulics (liquid inventory management), corrosion management (under deposit corrosion and/or top of line corrosion), and solid management such as wax, asphaltene, sand and scale control. Asoil/gas production continues to move to deep sea areas, pigging of long subseatie-backs to manage liquid holdup and terrain slugging/liquid surge due to longrisers is a significant flow assurance challenge. Production risks include aseparator trip caused by a surge in liquids/solids into topsides equipment, andthe potential for lost production due to a stuck pig in the offshoreflowline. In order to minimize the liquid/solid surge risks in long flowlines/pipelines, by-pass pigging has been used in various offshore flowlines. Properly designedby-pass pigging can effectively minimize the liquid/solid surge by spreadingthe collected liquid and/or scraped deposits in front of the pig, as shown inFigure 1. The gas bypassing the pig elongates the liquid slug in front of thepig by creating a certain void fraction in the slug. Several pigging models are widely used to predict liquid surge risk. It isimportant to validate the by-pass pigging models with field data to haveconfidence in surge capacity design; however, very few attempts have beenreported. Wu et al (1996) reported the size and duration of the liquid surgeobserved during a field test of by-pass pigging operations was about 30% of thepredicted surge volume. This paper presents liquid surge data collected during by-pass piggingoperations for a wet gas/condensate subsea flowline (ID = 20.6" and L = ~10 km)running from a wellhead platform to a central processing platform in anIndonesian offshore field. Field data from by-pass pigging operations arecompared to predictions from the OLGA model and to a model proposed by Fan andDanielson (2009). This field data and model prediction results are valuable todevelop liquid surge prediction practices for wet gas/condensate flowlines. These results will help pipeline design engineers to accurately estimate therequired surge capacity and will assist production staff to optimize pipelinepigging operations.
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.
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) | 0,000 | 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 découle