Dynamic vs. Static Ranking: Comparison and Contrast in Application to Geo-cellular Models
Notice bibliographique
Résumé
Abstract With increasing computational power, large geo-cellular models are constructed comprising of multi-million cells. Using uncertainty work flows, large number of geo-cellular models (realizations and scenarios) are constructed which can incorporate uncertainties in static parameters. The typical number of geo-cellular models can range between as few as thirty to as high as hundreds or even thousands. Once these models are constructed, the geo-modeler as well as simulation engineer realizes that all these models cannot be flow simulated without significant upscaling. Therefore, a search begins to find a method which would cost effectively select just a few of these geo-cellular models which can bracket the uncertainties present in these models. Static methodologies, which rank these realizations based on static properties such as Hydrocarbon Pore Volume (HPV) or STOIIP/GIP may not truly represent the ranking based on actual performance of the reservoirs. For example, a reservoir which contains large HPV but is not well connected will ultimately produce less oil/gas than a reservoir which is well connected but contains less HPV. We, therefore, need a ranking based on dynamic characterization of the reservoir. We have developed methodology based on Eikonal equation which can rank multiple realizations extremely efficiently. The method determines the time it takes for a traveling of a pressure "wave" to reach a particular grid block from a given well and assumes that time it takes to "tag" a grid block will reflect how quickly that grid block can be drained from a given well. Unlike finite difference or streamline simulation, the method does not require solving any matrix; hence, it is unconditionally stable. In matter of few minutes, a geo-cellular model containing more than fifty million cells can be interrogated. Using both synthetic and field examples, we demonstrate that the ranking based on our methodology is consistent with the ranking one would obtain using finite difference simulator. Further, ranking based on static properties does not correlate well with the dynamic response of the reservoir. By using the proposed methodology, limited number of geo-cellular realizations can be selected which can capture true dynamic uncertainty in reservoir performance.
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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 ».