Permeability Prediction in One of Iraqi Carbonate Reservoir Using Statistical, Hydraulic Flow Units, and ANN Methods

Authors

  • Ohood Salman Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
  • Omar Falih Hasan WASM: Energy and Chemical Engineering, Curtin University, WA, Australia/ Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
  • Sameer Al-Jawad Ministry of Oil, RFD, Baghdad, Iraq

DOI:

https://doi.org/10.31699/IJCPE.2022.4.3

Keywords:

permeability, FZI, Artificial Neural Network, Mishrif formation

Abstract

   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.

Author Biographies

  • Ohood Salman, Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

     

     

  • Omar Falih Hasan, WASM: Energy and Chemical Engineering, Curtin University, WA, Australia/ Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

     

     

  • Sameer Al-Jawad , Ministry of Oil, RFD, Baghdad, Iraq

     

     

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Published

2022-12-30

How to Cite

Salman, O., Hasan, O. F., & Al-Jawad , S. (2022). Permeability Prediction in One of Iraqi Carbonate Reservoir Using Statistical, Hydraulic Flow Units, and ANN Methods. Iraqi Journal of Chemical and Petroleum Engineering, 23(4), 17-24. https://doi.org/10.31699/IJCPE.2022.4.3

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