Recent Advances in Seismic Monitoring of Thermal EOR
Notice bibliographique
Résumé
Abstract Well-planned and executed reservoir surveillance has proven to add significantly to the production and ultimate recovery of hydrocarbons, notably in areas of Improved and Enhanced Oil Recovery (IOR/EOR). Recent technological advances in the area of data acquisition and integration have led to increased use of well and reservoir surveillance data to optimize such processes. In the case of thermal EOR, one of the most important subsurface uncertainties impacting performance is heat and steam front conformance, both vertically and arealy. This paper illustrates new geophysical technologies used for monitoring various thermal EOR recovery strategies in The Netherlands, Canada, and Oman. We focus on permanently buried seismic sources and receivers, refraction seismic, down-hole seismic, and the newly developed Distributed Acoustic Sensing (DAS) to enable low-cost and non-intrusive seismic surveillance. These technologies are not without challenges, but our field trials indicate they have the potential to broaden the successful application of reservoir monitoring onshore. Introduction Enhanced Oil Recovery (EOR) is well established as a tool for increasing recovery beyond secondary production, extending the life of fields, and postponing field abandonment activities and costs. Thermal EOR in particular has been instrumental to unlocking resources that may not be producible otherwise (e.g., large bitumen resources in Canada), and increasing recovery from heavy oil fields that have already undergone waterflood. Currently, thermal EOR is done only onshore, and for relatively shallow reservoirs (e.g., up to 1 km depth - a limitation imposed by heat losses that would condense steam to hot water for deeper injection wells). To optimize EOR, one must monitor reservoir changes induced by the treatment. Seismic monitoring is attractive for its ability to sense a large volume of the subsurface, illuminating it between and away from existing wells. It has proven useful for reservoir monitoring offshore (e.g., Hatchel et al., 2013; Stammeijer et al., 2013; El Ouair and Strønen, 2006; Osdal et al., 2006; Howie et al., 2005; Whitcombe et al., 2004). However, it has been slow to mature onshore, both because the Value-of-Information hurdle is higher in fields with high well density (typical for mature onshore developments) and because of a number of technical and non-technical challenges. The technical challenges include spatial and temporal variability of the near surface, changes in source and receiver coupling to the formation, complex and evolving infrastructure, high noise levels caused by activities above ground, and the relatively high cost of seismic operations in forested or populated areas. Non-technical challenges may include land access (e.g., restrictions due to competition with other uses such as farming or aboriginal activities), regulatory and environmental issues (surface footprint) and public perception of risk.
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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 ».