The Sanding Mechanisms of Water Injectors and their Quantification in Terms of Sand Production: Example of the Buzzard Field (UKCS)
Notice bibliographique
Résumé
Abstract The sanding of water injectors is considered a serious issue, as it can trigger important injectivity reductions and may sometimes and may sometimes lead to the collapse of wells. Cross-flow during shut-in and the water hammer pressure wave generated by the well closure are recognized as the main contributing factors to sanding. However the paper will show that the precise mechanisms of sanding on water injectors have not been fully described yet. The unexpected and early collapse of an injector on the Buzzard field - i.e. the largest current oil producer in the UK - required the evaluation of the sanding risk for the other 12 injection wells, as the loss of another one would have been critical in terms of field management. The complete records of all the wells, including their injection histories were therefore recovered and analysed. The analysis revealed that four sanding mechanisms were at play: Natural cross-flow between layers at pressure equilibrium, Forced cross-flow between layers not at pressure equilibrium, Swabbing from the water-hammer pressure-wave, Surface-flow between wells. The occurrence of each mechanism for each well was checked and quantified through field data analysis, modeling and direct downhole measurements (injection logs and video). In particular, the amount of sand produced by each mechanism was quantified. The analysis showed that the forced cross-flow on the collapsed well had produced sand quantities orders of magnitude larger than what was experienced by the other wells and the risk of losing another well was therefore judged minimum. In addition measures were taken to limit the impact of all four mechanisms on the existing wells and a methodology was devised to avoid the conditions of the collapsed well on future wells. The paper presents a complete methodology for quantifying the risk associated with the sanding of injectors. In addition the measures taken to limit the impact of the various sanding mechanisms can easily be implemented without significant costs to all wells injecting in weak reservoirs.
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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 ».