Upscaling a 900 Million-Cell Static Model to Dynamic Model of the World's Largest Clastic Oil Field - Greater Burgan Field, Kuwait
Notice bibliographique
Résumé
Abstract The Greater Burgan field is the world's largest sandstone oil field. It has been producing since 1946 under primary depletion from natural water drive. Sub-surface modeling is an integral part of reservoir management and Kuwait Oil Company (KOC) has been investing significant amount of resources in this technology to support field development planning and depletion strategy. In 2001, the first comprehensive Greater Burgan full-field geological model was built with 65 million cells encompassing all the major reservoirs. Subsequently, a reservoir simulation study with a 1.6 million cells dynamic model was conducted in 2003 utilizing parallel simulation technology. In the last decade, active field development plans have resulted in major surface facility upgrades and more than 300 new wells drilled. The existing sub-surface models no longer sufficed to meet technical requirements and as a result, an unprecedented Greater Burgan sub-surface modeling project was commenced in 2009. This is a 4-year project consisting of structural, static and dynamic modeling. It started with Sequence Stratigraphy Study followed by Geo-modeling. The latter was completed in August 2011 and subsequently paved way to Dynamic Modeling phase of the study. This paper discusses up-scaling of the high resolution geological model and the specific problems that the study team had to overcome in the process. State-of-the-art technologies were applied to the construction of the biggest-ever geological model (900 million cells) of the Greater Burgan field. The high resolution of the static model was necessitated by not only the sheer size of the field, but also, by the complex depositional environment defining the internal architecture of the reservoir and the resultant heterogeneity in the system. Sedimentological and stratigraphic data were used extensively to describe the internal architecture of the reservoir, capturing the level of heterogeneity observed in the field. A primary use of this high resolution model was to create a basis for the flow simulation model used in reservoir management. Although computing technology has advanced significantly, conducting flow simulations on such a fine scale model demands prohibitive amount of computation and becomes impractical when a time constraint is imposed on the project. Therefore, model up-scaling is essential to conduct simulations in a reasonable run time. Preservation of volumetric quantities and flow features were the two key considerations for the successful up-scaling. While volumetric conservation can be achieved by following a strict procedure, preserving flow features across the various reservoirs imposes a great challenge. This paper addresses actual challenges encountered during the up-scaling process. The discussion focuses on the following topics: Choice of the model size considering both computational time and accuracy of simulation results. Need for multi- scale approach with three simulation models: Fine, Coarse, and Very Coarse - each to be used to answer specific questions of the study;Right balance between areal and vertical grid coarsening that ensures adequate model physics and preservation of geological features;Mechanistic modeling to support decisions made in the process of up-scaling;Preservation of flow features in various reservoirs, difference between massive and more heterogeneous reservoirs;Transferring water saturation between fine and coarse models: testing various approaches to find one that produces the best volumetric match.
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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 ».