Enumeration approach in condensate banking study of gas condensate reservoir

This paper presents Enumeration Method in gas condensate reservoir simulation to study the condensate banking complex physics phenomena. Initially, coarse scale grid is commonly used for gas condensate reservoir simulation study. Nevertheless, the coarse scale simulation disregards the condensate ba...

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Bibliographic Details
Main Authors: Fuad, I.I.M., Lee, J.H., Negash, B.M., Md. Akhir, N.A.
Format: Book
Published: IOS Press 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37639/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172819891&doi=10.3233%2fAERD230023&partnerID=40&md5=606c306d47690a714fb67dae87ccb99e
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Institution: Universiti Teknologi Petronas
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Summary:This paper presents Enumeration Method in gas condensate reservoir simulation to study the condensate banking complex physics phenomena. Initially, coarse scale grid is commonly used for gas condensate reservoir simulation study. Nevertheless, the coarse scale simulation disregards the condensate bank or it is not able to demonstrate the precise distribution and effects. By introducing Local Grid Refinement (LGR) in simulation model arguably brings a better representation of the condensate bank effect near wellbore but significantly increases the run time. This become severe especially in full field modeling with comingled production. Therefore, enumeration initialization approach was developed to divide the simulation explicitly in coarse scale simulation. During the stops, a region near wellbore was designed where condensate bank parameters were modified based on the history matching. Hence, the drastic change of well performance due to condensate banking could be captured. This drastic change could not physically described in conventional coarse scale simulation model, thus affect prediction accuracy. Comparison between enumeration ways with conventional approach were then investigated. It was found that enumeration method shows a better prediction in investigating the behavior. This is due to its ability to predict mobility changes due to condensate banking, consequently, improve the condensate bank characterization. © 2023 The Authors.