Permeability Prediction in One of Iraqi Carbonate Reservoir Using Statistical, Hydraulic Flow Units, and ANN Methods
DOI:
https://doi.org/10.31699/IJCPE.2022.4.3Keywords:
permeability, FZI, Artificial Neural Network, Mishrif formationAbstract
Permeability is an essential parameter in reservoir characterization because it is determined hydrocarbon flow patterns and volume, for this reason, the need for accurate and inexpensive methods for predicting permeability is important. Predictive models of permeability become more attractive as a result.
A Mishrif reservoir in Iraq's southeast has been chosen, and the study is based on data from four wells that penetrate the Mishrif formation. This study discusses some methods for predicting permeability. The conventional method of developing a link between permeability and porosity is one of the strategies. The second technique uses flow units and a flow zone indicator (FZI) to predict the permeability of a rock mass using data from cores and well logs. The approach is used to predict the permeability of some uncored wells/intervals. The flow zone indicator is an efficient metric for calculating hydraulic flow units since it is based on the geological properties of the material and varied geometries pore of rock mass (HFU) and Artificial Neural Network (ANN) analysis is another way for predicting permeability. The result shows the FZI method, gave acceptable results compared with the obtained from core analysis than the other methods.
Received on 06/06/2022
Accepted on 10/09/2022
Published on 30/12/2022
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