Velocity, attenuation, and microseismic uncertainty analysis of the Niobrara and Montney reservoirs
Notice bibliographique
Résumé
Time-lapse reservoir characterization with surface seismic provides greater spatial information about reservoir physical properties, and delineates reservoir scale changes. Identification of reservoir deformation due to hydraulic fracturing and production improve reservoir models by mapping non-stimulated and non-producing zones. Monitoring these time-variant changes improves the prediction capability of reservoir models, which in turn should lead to improved well and stage placement. In Wattenberg Field, the Reservoir Characterization Project (RCP) at the Colorado School of Mines (CSM) and Anadarko Petroleum Corporation (APC) collected time-lapse, multicomponent seismic data in order to characterize the reservoir fracture changes caused by hydraulic fracturing and production in the Niobrara Formation and Codell Sandstone member of the Carlile Formation. Three seismic surveys help understand the dynamic reservoir changes caused by hydraulic fracturing and production of eleven horizontal wells within a one-square mile section (Wishbone Section). A baseline survey was recorded immediately after the wells were drilled, another survey after stimulation, and a third survey after two years of production. A robust layer stripping method is used to quantify 4D velocity and attenuation from pre-stack seismic data. Processing of the data before attenuation analysis includes noise reduction, regularization of amplitudes, and statics. Data show that time-lapse, pre-stack velocity and attenuation estimates are sensitive to hydraulic stimulation and production. Time-lapse velocity and attenuation results are integrated with image logs, surface microseismic, tracer data, and production information to analyze how faults, joint sets, and well spacing, affect stimulation, early term production, and late term production of the eleven horizontal wells in the Wishbone Section. Data demonstrate that faults in the reservoir limit lateral stimulation and allow hydraulic fracture fluids to move to other reservoir facies vertically within the Wishbone Section. Attenuation and velocity changes are observed in the western portion of the survey. Higher producing wells are also located the western portion of the study area. Borehole microseismic is a common tool used to evaluate hydraulic stimulation. A challenge in microseismic monitoring is quantification of survey acquisition and processing error, and how these errors jointly affect estimated locations. Quantifying error and uncertainty has multiple benefits, such as more accurate and precise estimation of locations, anisotropy, moment tensor inversion, and, potentially, allowing for detection of 4D reservoir changes. Processing steps are applied to a downhole microseismic dataset from Pouce Coupe, Alberta, Canada. A probabilistic location approach is implemented to identify the optimal bottom well location based upon known source locations. Probability density functions (PDF) are utilized to quantify uncertainty and propagate it through processing, including in source location inversion to…
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 | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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 ».