Analysis and Optimization of Zama Field Development Using Integrated Production System Modelling
Notice bibliographique
Résumé
Abstract The Zama field, located offshore of Mexico, is one of the world's biggest shallow-water oil discoveries in the past 20 years. The field has recoverable volumes of over 700 MBBL of oil. The development plan will include 2 production platforms, 29 oil producers, and 17 water injectors with two 66 km long pipelines to carry the oil to an onshore facility. Given the significance of this field, the use of an IPSM (Integrated Production Systems Modelling) can provide an increased value from this development through better design and operational decisions. The reservoir has conventional 28 °API oil, located in a thick pay with a significant geothermal gradient - Which will have an impact on the performance of the waterflood as water properties will change with temperature. This requires thermal capability in the reservoir simulator. The wellbore and facilities models, including all relevant completions and equipment, were built in a steady-state integrated production system module, that is capable of handling any fluid model. For this project, thermal black oil in both reservoir and facilities was considered the right approach as it will have a better simulation performance than full EOS models. The integrated simulation used explicit coupling between the reservoir and production models, with a "smart" coupling frequency chosen by the integration tool. The workflow allows for multi-fidelity solution to IPSM - and this was utilized in areas such as well and pipeline models where there was a choice to use pressure drop correlations or pipe tables. Even though this is a greenfield development, the analysis had shown that water injection should begin from the start of field development. The base IPSM considered all these aspects, and was optimized for performance. Thereafter, the production and injection strategy (constraints, rates, scheduling, etc.) as well as the overall completion and facilities design (well tubings, pumps, pipeline, risers, etc.) were optimized in an integrated fashion - providing a range of outcomes from the chosen schemes. The workflow yielded a stable IPSM system capable of predicting long-term performance of the Zama field development plan. The workflow was able to integrate subsurface and surface disciplines on a collaborative platform, which drastically reduced the logistical and workflow inefficiencies that exist in traditional IPSM workflows. The advanced fluid handling capabilities, with thermal black oil models in both reservoir and production system proved valuable to enhance the predictability from the model. The workflow captured the complex interactions between facilities and reservoir and the entire system was optimized using a novel end-to-end uncertainty management framework.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 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 ».