Surface-Geometry and Trend Modeling for Integration of Stratigraphic Data in Reservoir Models
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Notice bibliographique
Résumé
Abstract Accurate prediction of reservoir performance depends on an accurate estimate of the subsurface structure, lithofacies, associated petrophysical properties, and fluid distribution. Often the reservoir heterogeneities that are controlled by stratigraphic architecture and sedimentological trends are difficult to predict, particularly at subseismic resolution and far from well control. An ongoing challenge in subsurface modeling has been the utilization of analogs of complex geology along with seismic and sparse well data to predict the natural geologic complexity in models of the subsurface. Time surfaces provide very important constraints on the geometric connectivity and continuity of facies and petrophysical properties in reservoirs. Such time surfaces are a suitable framework for facies and petrophysical properties modeling. Instead of modeling each reservoir later as a whole, the elementary sediment units are more easily modeled separately; the final composite model will show realistic heterogeneity patterns consistent with the underlying physics. This work presents a hybrid deterministic, rule-based, and stochastic technique to generate surface models. These surface models are utilized as a framework to preserve sediment trends and honor analog and well data. Petrophysical properties are modeled for each sediment unit to reproduce trends. Finally, the individual sediment units are assembled into a reservoir model. The surface model is created stochastically with parameterized surface templates. The shape, extent, height, orientation and regularity of the surfaces are controlled by user-specified distributions. The location of each surface in the reservoir is chosen on the basis of previous events. The addition of each surface is based on sedimentological rules. Conditional Gaussian simulation is used to ensure that the surfaces reflect realistic uncertainty through undulations and that well data intersections are honored. The surface model divides the reservoir layer into sediment units. From geology and well data, trends are parameterized with mathematical functions as trend templates. Residuals are characterized after removing the trend. For each sediment unit, a trend and a residual model are generated stochastically. The observed well logs serve as conditioning data to guide the deployment of trends and to condition the generation of residuals. The model of each sediment unit combines its trend plus residual. The final reservoir model is obtained by assembling the separate sediment units.
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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 | 0,001 |
| 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 découle