Abstract
Permeability determination in Carbonate reservoir is a complex problem, due to
their capability to be tight and heterogeneous, also core samples are usually only
available for few wells therefore predicting permeability with low cost and reliable
accuracy is an important issue, for this reason permeability predictive models become
very desirable.
This paper will try to develop the permeability predictive model for one of Iraqi
carbonate reservoir from core and well log data using the principle of Hydraulic Flow
Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good
parameter to determine (HFUs).
Histogram analysis, probability analysis and Log-Log plot of Reservoir Quality
Index (RQI) versus normalized porosity (øz) are presented to identify optimal
hydraulic flow units. Four HFUs were distinguished in this study area with good
correlation coefficient for each HFU (R2
=0.99), therefore permeability can be
predicted from porosity accurately if rock type is known.
Conventional core analysis and well log data were obtained in well 1 and 2 in one of
carbonate Iraqi oil field. The relationship between core and well log data was
determined by Artificial Neural Network (ANN) in cored wells to develop the
predictive model and then was used to develop the flow units prediction to un-cored
wells. Finally permeability can be calculated in each HFU using effective porosity
and mean FZI in these HFUs. Validation of the models evaluated in a separate cored
well (Blind-Test) which exists in the same formation. The results showed that
permeability prediction from ANN and HFU matched well with the measured
permeability from core data with R
2
=0.94 and ARE= 1.04%.
their capability to be tight and heterogeneous, also core samples are usually only
available for few wells therefore predicting permeability with low cost and reliable
accuracy is an important issue, for this reason permeability predictive models become
very desirable.
This paper will try to develop the permeability predictive model for one of Iraqi
carbonate reservoir from core and well log data using the principle of Hydraulic Flow
Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good
parameter to determine (HFUs).
Histogram analysis, probability analysis and Log-Log plot of Reservoir Quality
Index (RQI) versus normalized porosity (øz) are presented to identify optimal
hydraulic flow units. Four HFUs were distinguished in this study area with good
correlation coefficient for each HFU (R2
=0.99), therefore permeability can be
predicted from porosity accurately if rock type is known.
Conventional core analysis and well log data were obtained in well 1 and 2 in one of
carbonate Iraqi oil field. The relationship between core and well log data was
determined by Artificial Neural Network (ANN) in cored wells to develop the
predictive model and then was used to develop the flow units prediction to un-cored
wells. Finally permeability can be calculated in each HFU using effective porosity
and mean FZI in these HFUs. Validation of the models evaluated in a separate cored
well (Blind-Test) which exists in the same formation. The results showed that
permeability prediction from ANN and HFU matched well with the measured
permeability from core data with R
2
=0.94 and ARE= 1.04%.
Keywords
Artificial Neural Network
Flow Zone Indicator
Hydraulic Flow Unit
Permeability Prediction