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How Powerful Material Balance Analysis Method for Predicting Gas Flooding Performance

Authors: H. R. Sutoyo; T. Ariadji; P. A. Aziz; M. L. Mahendra;

How Powerful Material Balance Analysis Method for Predicting Gas Flooding Performance

Abstract

Abstract Gas Flooding is one of the reservoir production management methods which commonly used in the petroleum industry in order to improve oil recoveries and maintaining reservoir pressure. This study discusses two methods in analyzing gas flooding, i.e., material balance analysis and reservoir simulation methods. The objective is to examine the ability and advantages of material balance method with respect to reservoir simulation. The studied field has three productive zones which consist of two light oil zones and one dry gas zone. Gas flooding scenario was conducted in one light oil reservoir and was modeled using both material balance and reservoir simulation methods to predict reservoir performance for about 14 years and the results were compared to investigate the effect of gas injection. The material balance method employs production, PVT, pressure data, and averaged properties of geological model for a single tank model. On the other hand, the reservoir simulation method uses a compositional model to investigate in detail the effect of injected gas to reservoir fluid behavior and the oil recovery The examinations indicate that the material balance analysis method shows the advantages of a requiring much less data, but could give reasonably accurate results. Furthermore, the application of material balance analysis in gas flooding could be used for gas injection rate optimization for a very short time manner and could assist for further development of reservoir simulation. For future development, material balance analysis could be used to analyze gas flooding scenario to save time and cost rather than reservoir simulation which takes longer time.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
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