An Innovative Procedure for an Integrated Reservoir Simulation Study for a Reservoir Below Its Oil Bubble Point Pressure
Notice bibliographique
Résumé
Abstract An innovative procedure was developed for an integrated reservoir simulation study for an oil field that is depleted below its oil bubble point pressure. The approach used was to complete a black-oil history match model, then convert the black-oil model to a compositional model to predict miscible or solution gas re-injection performance. An important element of the conversion to a compositional model is to preserve the history match results from the black oil model. This approach is unique in that equation of state (EOS) characterization and special core analysis results were used to determine the range of gas-oil ratios for a blackoil model history matching a reservoir pressure below its oil bubble point. Using the black-oil model for history matching helped reduce the CPU time requirement, thus resulting in quicker turnarounds. The converted compositional model was then used to develop future depletion strategies beyond primary production, such as solution gas re-injection or other EOR schemes. This paper describes the black-oil model setup, history match results and compositional model results for the base case and cycling produced gas schemes. The difference between the black oil and the compositional models is small for these two schemes because the produced gas-oil ratio is still within the validated GOR range. However the black oil model could not be used in the schemes for different injection composition streams, as the black-oil model can not utilize input compositions. Introduction Theoretically, a compositional model should be used to model a reservoir being depleted below its oil bubble point pressure, because the reservoir fluid undergoes compositional changes. However, a compositional model takes a lot of CPU time and a longer time to run, hence a black oil model is used hoping that the results are the same as the compositional model. From our previous experience(1), it is difficult to conduct a full field fully compositional simulation, hence a pseudocomponent model will be used. Again, there might be some deviation from the full field model. The purpose of this study is to develop an innovative procedure for (1) checking that the black oil model has the same results as the compositional model for a reservoir that is depleted below its oil bubble point pressure, and (2) confirming the pseudocomponent model gives a similar result to the fully compositional model. In this study, an equation of state (EOS) was used to characterize the reservoir fluid into a fully compositional model (FC) and then grouped into a 4 to 6 component pseudocomponent model (PC) to be used in the compositional reservoir simulator. In the meantime, a black oil PVT model was generated by the tuned EOS (fully compositional PVT model). To validate the EOS characterization, three reservoir simulation models were constructed using a core displacement data, black oil and two compositional models (FC and PC). After matching this special core analysis and available data such as recovery and injection pressure, the range of gas-oil ratios was determined for a black oil model that could be used in history matching a reservoir with pressure below its oil bubble point pressure.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».