Assessment and Prediction of Erosion in Completion Systems under Hydraulic Fracturing Operations Using Computational Fluid Dynamics
Notice bibliographique
Résumé
Abstract High rate injection or production of fluids with sand particles places wellhead components and downhole assemblies at risk of erosion damage. Depending on the severity and location of the material loss, this may pose a significant well loss or blowout hazard. For this reason, assessment and mitigation of erosion can be critical for such applications. In this work, Computational Fluid Dynamics (CFD) was used in conjunction with erosion models to assess the erosion damage characteristics associated with the operating conditions and equipment for a high-rate, shale gas reservoir fracturing application. The work was based on the severe erosion damage experienced by EnCana as a result of high rate hydraulic fracturing operations performed in horizontal shale gas wells at their Horn River, BC field development. Material losses were observed within the wellhead equipment as well as in the LTC couplings of the production casing string near surface in several wells. CFD models were developed for the existing wellhead and wellbore geometries and used to simulate a range of hydraulic fracture operating conditions in an effort to predict the locations and degree of material loss in the components in each case. The models were calibrated with caliper log data and measurements taken from casing samples retrieved from several wells. The analyses suggested that well head system modifications, such as tubing head spool changes and use of spacer spools, could be effective in substantially reducing material losses in the tubular connections. In addition, sensitivity analyses were performed for different wellhead configurations and variations in the hydraulic fracturing parameters to determine the factors that likely had the most influence on the connection material losses. The results served to demonstrate that it is possible to use CFD with erosion models as predictive tools to identify locations of severe erosion in completion systems, and, when calibration data is available, to quantify the amount of material loss in wellhead and downhole components. This information can aid in designing optimum completions systems, and in defining operating conditions which can reduce the risk of equipment failure, potential blow-outs, and associated safety and environmental hazards.
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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 ».