Gas Injection EOR Optimization Using Fiber-Optic Logging with DTS and DAS for Remedial Work
Notice bibliographique
Résumé
Abstract The evaluation of in-flow performance for all stages in pumping, multi-stage fractured horizontal (MSFHW) oil wells is challenging with conventional production logging tools. The successful deployment of fiber-optic sensing technology has been influential in diagnosing gas breakthrough in producing wells of a gas injection EOR project by providing real-time, full well in-flow profiling. The integrated operation of a multi-disciplined team and the outcomes will be described. An operator commenced a tertiary dry gas injection enhanced oil recovery (EOR) in the Bakken Formation of southeast Saskatchewan, Canada. The pilot project was designed as a 1:8 toe-heel injection pattern, with a one mile horizontal in-fill well supporting eight perpendicular producers. The offset producers are monitored using regular production data and gas breakthrough has been found in certain fracture stages that are in communication with the injector. A program was developed to use real-time fiber-optic DTS and DAS to identify the stages with gas channeling in the producer for corrective action. Distributed Temperature Sensing (DTS) utilizes the Joule-Thompson (J-T) cooling principle as the gas enters the frac port and expands into the liner, while Distributed Acoustic Sensing (DAS) captured the amplitude and frequency of acoustics from the fluid flow. Simultaneous measurements of DTS and DAS were taken using a hybrid fiber/electric wireline inside coiled tubing. The artificial pumping system was pulled out of the producer and coiled tubing was run to the bottom of the well. Real-time pressure and temperature sensors were run on the end-of the coil and a memory gauge at the bottom of the swab string. The well was stabilized overnight before swabbing and monitoring. A service rig was used to swab the well and establish a draw-down. Live down-hole DTS, DAS and pressure data were logged and monitored at the surface and remotely. Post-processed data interpretation from three wells in the EOR project will be discussed that shows DTS and DAS logging is a valuable technique in detecting injected gas channeling. The results were effective in making decisions about shutting off the problem stages. DTS was most effective since gas has a higher J-T coefficient compared to oil and water. The DAS data supplemented and confirmed the DTS data. Having real time downhole pressure measurements allowed for regulating the swabbing rates to obtain the desired drawdown pressure. Gas injection EOR projects can be optimized using fiber-optic sensing while swabbing. It provides a new and effective tool in identifying gas breakthrough for remedial work.
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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 ».