Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska's Western North Slope
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Notice bibliographique
Résumé
Abstract An integrated asset modeling (IAM) approach has been implemented for the Alpine Field and eight associated satellite fields on the Western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir and facilities management options under existing and future operating constraints. Oil, gas and water production from these fields are processed at the Alpine Central Facility (ACF). A number of local constraints exist for the asset, such as the requirement that all associated gas be used for facilities power generation, gas lift or re-injection. All produced water must be re-injected and, for pipeline integrity reasons, must be segregated from imported make-up sea water used for injection. Additionally, surface gas and water handling capacity is limited at the ACF. To further complicate matters, gas injected for EOR purposes is enriched such that it is miscible or near-miscible at reservoir conditions. These conditions create a unique and changing relationship between the oil, gas and water production, gas lift, miscible water alternating gas (MWAG) injection, lean gas injection, facilities constraints and injection availability. The scope of the current IAM project has been multi-fold. Optimization of oil production across all WNS fields requires the placement of injection fluids be simultaneously optimized. The optimization procedure begins by allocation of oil production targets based on current operating conditions, the potentials of the wells in each field to deliver fluids, and total gas lift availability. Excess gas compression capacity is utilized for gas lift and is allocated via an incremental gas-oil ratio sort on the production wells. Given the constraints on water injection noted above, optimization of injection fluids begins by determining pump requirements for produced water and the optimal field or injection manifold placement of the produced water. Following this, optimized placement of the miscible injectant (MI) and lean gas injectant (LGI) is determined based on a dynamic MWAG scheduling methodology developed to maximize oil recovery and ensure the number of gas injection wells have sufficient capacity to inject the required volume of gas in each reservoir. The volumetric split of gas into MI and LGI streams falls out directly from the specification of a target minimum miscibility pressure (MMP) constraint for the MI and the volume of condensates driven off the top of the condensate stabilizer column at the process facility. Finally, the volume of the make-up fluid (sea water) is determined based on the minimum of the remaining pump capacity or potential of the remaining wells to inject the water and allocated to each field based on a fractional oil voidage replacement scheme. Maximizing production across multiple fields necessarily requires that the best player (well) plays, regardless of the field to which it belongs. This requirement relates to both instantaneous production as would be considered under a gas lift optimization scenario as well as the longer term MWAG performance and recovery of each individual well pattern across all the fields. The IAM technology utilized for managing the WNS fields consists of full-field compositional reservoir simulation models for each reservoir integrated with a pipeline surface network model and a process facility model. Spreadsheet based allocation routines and advanced mathematical coupling algorithms complete the IAM model enabling not only the prediction of the assets’ performance under the aforementioned constraints, capacities and operating conditions, but to optimize overall performance and analyze the impact of decisions. To the authors’ knowledge, this is the first time integrated asset modeling has been applied to bring the entire production stream including reservoir, wellbore, surface network and process simulation together for planning and managing MWAG injection to optimize recovery from an existing development.
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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)
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Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
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