Comparison of Riser and Well System Response Predictions to Full-Scale Measurements in a Shallow Water Harsh Environment
Notice bibliographique
Résumé
Well system fatigue accumulated during drilling operations from mobile offshore drilling units (MODUs) has been a topic of intensive study in recent literature. A variety of efforts from individual operator studies to industry-wide joint industry projects have been launched in an effort to improve understanding of both the load and resistance elements of well system fatigue assessment. A key uncertainty in current predictive modeling practice is the level of conservatism inherent in that modeling, as compared to real-life system response. Collection of good quality, full-scale field measurement data is a key component in evaluating this uncertainty. A full-scale measurement campaign was recently conducted on a 6th generation MODU during drilling operations in a shallow water region with a harsh environment. The measurement system on board the vessel captured vessel, riser and lower marine riser package (LMRP) accelerations and angular rates, along with riser tensioner pressures. Concurrent environmental measurements were acquired via wave rider buoy and acoustic Doppler current profiler (ADCP) located near the mobile offshore drilling unit (MODU), capturing wave height, period and direction, and full-depth current profiles. This paper focuses on data collected for one well during the winter months where significant wave heights at the drilling site varied from 6ft to 26ft. In this paper, a comparison between measured and predicted riser and well system loading is presented. The predicted riser and well system response is based upon global riser analyses performed in both the frequency and time domains to evaluate the effect of linearization in the modeling process. Loading demand on the well system is also presented as cycle-range histograms. This paper also summarizes the conclusions from the comparisons of these measurements with other published measurement campaign data. While loading derived from previous riser analysis studies have been found to be “conservative” as compared to full-scale measurements, this study finds that the predicted and measured responses match quite well. Furthermore, it is also shown that the frequency domain results are adequate for this study, suggesting that the nonlinearity within the riser and well system for the conditions studied is relatively minor.
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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 | 0,000 | 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 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 ».