Satellite Monitoring of Cyclic Steam Stimulation without Corner Reflectors
Notice bibliographique
Résumé
Abstract Surface movement of a field under cyclic steam stimulation (CSS) is induced by the thermal processes used to extract bitumen from the reservoir. The ground movement can be related to reservoir dilation and compaction and provides a record of the effect of injection and production. Accurate monitoring of the ground deformation that occurs at a CSS field can be used to calibrate predictive models, show the effectiveness of steam injection, and demonstrate the impact of the recovery operation on the surface elevation. Accurate monitoring, however, requires the ability to consistently measure large non-linear changes in the surface height over relatively small areas. CSS sites can experience 20+ cm of surface heave in one month. The spatial extent of the ground movement is related to the well layout. Along short distances, 10s of metres, the ground movement (heave and subsidence) may be more than 2 cm. Interferometric Synthetic Aperture Radar (InSAR) uses radar returns from the ground to calculate very precise estimates of the ground change. In arid regions, InSAR can be used to capture a very high density of ground movement points. In this case, measurements with a density of approximately every 3 m are possible with the RADARSAT-2 satellite. The ground conditions in arid regions are ideal for radar observation. The ground conditions in the region of the Alberta oil sands are not ideal for InSAR monitoring. The amount of ground water, variations due to seasonal change, and the sparsity of effective radar reflectors led to the development of InSAR methods that employ installed targets (or corner reflectors). This methodology works very well to capture the relatively slow ground change associated with steam assisted gravity drainage (SAGD) operations. The same measurement process at a CSS operation would require an extreme density of corner reflectors. It would not be environmentally or economically feasible to install corner reflectors at a spacing of 75 m across a CSS operation. To improve the accuracy of InSAR surface elevation monitoring, we have created a new way of extracting deformation information from radar imagery. This has worked particularly well for the CSS process at the Primrose field. The methodology extracts the signal in high-noise conditions and dramatically increases the effectiveness of InSAR observations over a CSS field. The new InSAR methodology exploits the spatial and temporal characteristics of the radar images to produce a more robust estimate of the ground movement than had been possible previously. The wide-area InSAR measurements are compared to measurements from Global Positioning System (GPS) and to the net over injection (NOI) metric, which describes the amount of steam going into the reservoir less the amount of produced fluids. The GPS time series and the InSAR measurements show excellent agreement. The point source nature of the GPS data, however, restricts the spatial comparison that is possible. In this case, the NOI data shows the progression of injection / production spatially and temporally. The ground movement patterns from the NOI and the InSAR data are very similar. Increases in NOI are very highly correlated with surface heave. Subsidence seems to lag decreases in NOI slightly but is still clearly positively correlated.
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 | 0,000 |
| 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 ».