Well Integrity Monitoring & Analysis Using Distributed Acoustic Fiber Optic Sensors
Notice bibliographique
Résumé
Abstract Evaluating well integrity (i.e. flow of fluids or gas) from behind casing can be challenging using existing single mode analog sensors; they offer limited representation and data acquisition can be time consuming. Further, traditional processing algorithms such as Fourier Transforms are not responsive to non-stationary, nonlinear events such as random, low volume leak signatures. Recent advancements in both fiber optic Distributed Acoustic Sensors (DAS), and processing algorithms stand to significantly simplify downhole low rate leak detection. This paper will explain the capabilities and limitations of this monitoring approach. Distributed Acoustic Sensors; Proven in demanding applications such as submarine sonar systems, optical fiber can be packaged in such a way that makes it extremely sensitive to acoustic disturbances along its entire length. Using the fiber itself as a sensor has several advantages, some of which include; extreme sensitivity, design simplicity, and the ability to obtain 1000’s of simultaneous measurements with little or no loss of fidelity. Datasets were obtained from both a specifically designed 200 ft vertical controlled test well simulator and actual problematic gas wells in Canada. Processing Algorithms: Using DSP techniques and real time methods, the engineer can tune the system to a specific leak signature which eliminates unwanted events and highlight useful acoustic components pertaining specifically to the leak. Once the data is obtained the high fidelity acoustic data undergoes various filtering and error detection processing. Algorithms were tested in Matlab and converted to executable code once verified. The integrated well monitoring and analysis system offers a more comprehensive, detailed solution. When compared to traditional technologies, future remedial strategies were often strategically more accurate using the fiber based systems, especially when low leak rates were involved. It is anticipated that engineers will be able to locate problematic leaks with higher confidence and save money by reducing the number of failed interventions; similarly, the need for experienced highly trained log analysts will be reduced. Applications for this information may include: low rate leak detection through casing, pipe integrity failures, zonal isolation issues, long term well monitoring, carbon storage and sequestration, evaluating intervention effectiveness, and locating multiple source leaks along a wellbore.
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.
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 | 0,001 |
| 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 | 0,001 |
| 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 ».