Dynamic Reservoir Characterization and Production Optimization by Integrating Intelligent Inflow Tracers and Pressure Transient Analysis in a Long Horizontal Well for the Ekofisk Field, Norwegian Continental Shelf
Notice bibliographique
Résumé
Abstract When analyzing well performance in carbonate reservoirs, the traditional approach usually requires the best practices from pre and post stimulation analysis. Most techniques require an understanding of production performance, which can be divided into two categories. The first is related to reservoir performance away from the wellbore i.e. permeability, fracture network, reservoir pressure, boundaries and secondly, the near wellbore and zonal contribution i.e. permeability-thickness, skin, oil and water influx from individual producing zones. In order to develop a full picture of how these two categories contribute to production performance, a detailed analysis should be conducted to understand their interaction. Low permeability carbonates and chalk fields often require long multi-stage frac'ed horizontal wells which further complicates the analysis due to lack of measured data in each stage. The Ekofisk filed development is a mature water flood, which includes both deviated and horizontal wells. Deviated wells are placed in the more crestal location, while the horizontal wells are generally placed towards the flanks where reservoir properties are of lower quality as compared to the field's crest. Production performance and optimization is largely dependent on efficient zonal stimulation, well and reservoir management. Understanding the distribution of fluid phases along the well, especially the water influx, may enable timely executed water shut-offs to mitigate water breakthrough. The traditional technique of understanding where and how much oil and water are being produced, require well intervention through production logging (PLTs). Well interventions are often difficult to execute due to limited access to platforms, the high cost of wells and production deferments. All of these factors limit efficient production optimization due to the inability to collect data in a timely manner for analysis. Furthermore, experiences from the Ekofisk field indicate that PLT data often gives inconclusive results due to known challenges of interpreting PLT data from horizontal wells. An intervention free and cost efficient approach using inflow tracers has been piloted to acquire early time data, in addition to acquiring well and reservoir understanding throughout the well life. This approach was successfully developed and tested in a newly drilled horizontal Ekofisk field producer. The well was equipped with inflow tracers permanently installed in the completion string to identify individual zone's production contribution including the split by oil, gas and water. In addition, unique intra well tracers were injected into each zone during stimulation to gain knowledge of the stimulation efficiency. During well start up, clean out, transient and post transient production periods extensive sampling programs were executed. As a result, sufficient data has been acquired in order to complete reservoir characterization analysis together with traditional Pressure Transient Analysis (PTA), and then followed by production optimization. The acquired tracer data and interpretation has been compared with conventional PLT interpretation to verify the former. This is the first integrated application using permanently installed inflow tracers, injected intra well tracers and pressure data interpretation solution for reservoir characterization and production optimization performed.
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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 | 0,001 | 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.
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