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- W2053171489 abstract "Abstract Knowledge of permeability is critical for developing an effective reservoir description. Permeability data may calculated from well tests, cores and logs. Normally, using well log data to derive estimates of permeability is the lowest cost method. In the last years, the concept of hydraulic flow units (HFU) has been used in the petroleum industry to improve prediction of permeability in un-cored intervals/wells. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir quality index (RQI). Both measures are based on porosity and permeability of cores. It is assumed that samples with similar FZI values belong to the same HFU. This paper will focus on the evaluation of formation permeability in un-cored intervals for a carbonate reservoir in Iran from core and well log data. We used Flow Zone Index method for rock type identification and Artificial Neural Networks for permeability estimation. Identifying the hydraulic flow units that is the first step of predicting permeability always takes lots of time and lack appropriate accuracy. We have developed a new clustering technique that is more precise, easy to apply and taking much less time." @default.
- W2053171489 created "2016-06-24" @default.
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- W2053171489 date "2010-07-31" @default.
- W2053171489 modified "2023-10-18" @default.
- W2053171489 title "Permeability Prediction of Un-Cored Intervals Using New IMLR Method and Artificial Neural Networks: A Case Study of Bangestan Field, Iran" @default.
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- W2053171489 doi "https://doi.org/10.2118/140682-ms" @default.
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