Start-Up Optimization in Deep-Water Developments: Strategies for Managing Wax, Hydrates and Slugging
Notice bibliographique
Résumé
Abstract Objectives/Scope The XYZ-Field, located at Eastern offshore India, at water depth of 600 meters, features deep-water oil wells tied back to a centrally located FPSO via dual 10″ pipe-in-pipe (PIP) flow lines. Ensuring uninterrupted flow during the initial start-up phase is crucial due to challenges such as low ambient temperatures, slugging, wax deposition, hydrate formation and flow instabilities. Apart from these, well-tested results indicated a lower GOR, further complicating things. This paper presents a comprehensive set of flow assurance strategies designed to mitigate these risks and ensure a smooth production ramp-up. Methods, Procedures, Process Flow assurance simulations using OLGA and PIPESIM were conducted to evaluate transient start-up behaviour, wax deposition potential and slugging tendencies. The analysis identified several key challenges, including very low fluid temperatures at the time of well ramp-up which creating challenges like hydrate formation, wax deposition and crude gelation another issue was low flow rates at the time of wells ramp-up which leads to low thermal mass so fast decline in fluid temperature and slugging issue in pipeline which also helping in making cold spot in pipeline and leading to more wax deposition. To counter these challenges, an innovative start-up strategy was developed, which includes preheating of the line, increasing fluid flow at the time of start-up, choking at FPSO topside and optimising the start-up sequence of wells. Results, Observations, Conclusions With the help of this strategy we were able to maintain the temperature of pipeline throughout startup period above Wax appearance temperature which helped in avoiding wax deposition, crude gelation and also helped in avoiding hydrate formation another thing we eliminated the slugging using FPSO topside choke and by increasing flowrate which helped in getting stable flow at FPSO and no issue to equipment at the topside and this also stop making cold spot in riser which is more prone to wax deposition so resulted in less wax deposition. This strategy also helped in long-term operation of the field by minimizing the slugging and wax deposition, resulting in decreased frequency of mitigation measures to remove wax in the flowline. Novel/Additive Information These findings provide a robust framework for enhancing the reliability of deep-water oil field start-ups, reducing production downtime, and ensuring long-term flow assurance. The proposed innovative strategy for reducing the frequency of wax mitigation measures presents a significant improvement in operational efficiency while maintaining flow line integrity.
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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 | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 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 ».