Unsupervised PSD Clustering to Assess Reservoir Quality Along the Horizontal Wells: An Efficient Inflow Control Devices Design
Notice bibliographique
Résumé
Abstract In steam-assisted gravity drainage (SAGD) operations, inflow control devices (ICDs) might provide an extra pressure drop (ΔP) on top of the liquid pool's ΔP. To avoid hot-spot zones, this ΔP design heavily relies on reservoir quality. Flow-loop experiments can provide flow قate measurements versus ΔP for various nozzle designs. Therefore, an efficient ICD design should be investigated in a numerical flow simulation that represents reservoir quality and heterogeneity by employing flow-loop data. In this study, core analysis and 40 PSD data drilled in the same location are collected, and permeability for each PSD is estimated using a correlation developed in our previous study. Given PSD offers a measure of hydraulic properties and heterogeneity, it can provide an indirect indicator of potential hot-spot zones. Moreover, representative PSDs are determined by using a clustering algorithm to tie the best-designed ICD to the relevant geology. The reservoir model for the database's location is generated using real data, three tabular data from flow-loop experiments are assigned to the reservoir simulation, and the ICDs' performances are compared. The clustering algorithm generated five groups with a weighted average permeability of 4,013 mD. The first and second largest clusters with 6.55% and 35.05% fines content cover 55% and 23% of the database, respectively. By employing a relatively conservative production with subcooling between 10°C and 15°C, the cases with liner deployed (LD) ICDs offered a greater oil production rate, better steam conformance, and lower cumulative steam oil ratio (cSOR) than the cases without ICDs. However, in a rather risky production scenario with subcool between 1°C and 5°C, the case without ICDs could not be simulated in the desired the subcool temperature. Because of its enhanced steam conformance and slightly higher oil production rate, LDICD#1 was picked as the best case for the two scenarios. Compared to the case without ICDs, the oil production rate and cSOR for the case with LDICD#1 at higher subcool temperature rose by 17% and reduced by 8%, respectively. Compared to the case without ICDs, the oil production rate and cSOR for the case at lower subcool temperature with LDICD#1 raised by 21% and reduced by 12%, respectively. The findings demonstrate the effectiveness of ICDs at various subcool levels. The results could be applied in SAGD projects to reduce greenhouse gas emissions by reducing the water and natural gas usage to generate steam. Completion and production engineers would benefit from a better understanding of production relative performance to develop more effective operations design.
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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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».