Application of Integrated Production System Modelling (IPSM) for Long-Term Production Forecasting and Optimization, a Case Study in Deepwater Assets, Malaysia
Notice bibliographique
Résumé
Abstract In typical integrated simulation projects involving multiple reservoirs connected to a single producing facility, poor communication between production and reservoir engineers, who use different analysis tools, often leads to unreliable results. CoFlow is an integrated production system modelling (IPSM) tool and platform that helps RE's and PE's overcome these challenges. This work studies how CoFlow was used to provide robust, long-term (10 years plus) production forecasting and optimization for a deepwater oil development in Malaysia. Currently ongoing deepwater projects off the coast of Sabah are critical to sustaining Malaysia's crude oil output. These projects face high costs due to specialized equipment and subsea infrastructure installation, making it a necessity to simulate the complete fluid journey from the subsurface all the way to the oil platforms to ensure engineering design and consistency during forecasting. One such project, henceforth named Field G, has been built as an IPSM model in CoFlow. An IPSM model includes the reservoir model(s), the wellbore models as well as the piping and equipment that form the asset's surface network. Field G IPSM model was used to couple two reservoirs and link them to a complex surface network system, whereby the produced gas was separated and re-injected into the reservoirs using a custom algorithm. The IPSM model was operated using network-level constraints, which mimic the maximum fluid handling capacities of certain equipment on the production platforms. This is a unique and often overlooked capability of IPSM models, and it helps to make sure that the system is not producing beyond the limitations imposed by its surface network. The model was run for simulation times greater than 16 years, enabling forecasts that reach critical junctures in the field life such as the end of a PSC. Furthermore, maximum gas and liquid rate constraints were imposed on the models and various well operating scenarios were assessed to find the most optimum solution. All this could not be captured with just regular reservoir simulation, hence showcasing the value and importance of IPSM for large offshore projects. This was the first time that the G field was collaboratively modelled using IPSM approach and used to simulate forecast periods longer than 10 years. The CoFlow platform provided fast runtimes which allowed the authors to run multiple prediction scenarios and saved many man hours. Moreover, the IPSM model helped capture the complex interactions between facilities and reservoir performance through integration and multi-disciplinary collaboration.
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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 | 0,001 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».