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

Ohood Salman; Omar Falih Hasan; Sameer Al-Jawad

doi:10.31699/IJCPE.2022.4.3

Article Details

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

Authors:

Ohood Salman | Omar Falih Hasan | Sameer Al-Jawad

Publisher:

University of Baghdad

Journal:

Iraqi Journal of Chemical and Petroleum Engineering Vol.23 Issue 4, pp.17-24

Document Type:

Article

Publication Date:

12/2022

Document Language:

English

Document ID (Org No.):

2001368323

pISSN:

2618-0707

DOI:

10.31699/IJCPE.2022.4.3

Keywords:

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.

Article Details

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

Authors: Ohood Salman | Omar Falih Hasan | Sameer Al-Jawad

Publisher: University of Baghdad

Journal: Iraqi Journal of Chemical and Petroleum Engineering Vol.23 Issue 4, pp.17-24

Document Type: Article

Publication Date: 12/2022

Document Language: English

Document ID (Org No.): 2001368323

pISSN: 2618-0707

DOI: 10.31699/IJCPE.2022.4.3

permeability

FZI

Artificial Neural Network

Mishrif formation

Issues

Authors:

Ohood Salman | Omar Falih Hasan | Sameer Al-Jawad

Publisher:

University of Baghdad

Journal:

Iraqi Journal of Chemical and Petroleum Engineering Vol.23 Issue 4, pp.17-24

Document Type:

Article

Publication Date:

12/2022

Document Language:

English

Document ID (Org No.):

2001368323

pISSN:

2618-0707

DOI:

10.31699/IJCPE.2022.4.3