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Permeability prediction in one of Iraqi carbonate reservoir using hydraulic flow units and neural networks
Author
Source
Iraqi Journal of Chemical and Petroleum Engineering
Issue
Vol. 17, Issue 1 (31 Mar. 2016), pp.1-11, 11 p.
Publisher
University of Baghdad College of Engineering
Publication Date
2016-03-31
Country of Publication
Iraq
No. of Pages
11
Main Subjects
Topics
Abstract EN
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 R2 =0.94 and ARE= 1.04%.
American Psychological Association (APA)
al-Ubaydi, Dahlia Abd al-Hadi. 2016. Permeability prediction in one of Iraqi carbonate reservoir using hydraulic flow units and neural networks. Iraqi Journal of Chemical and Petroleum Engineering،Vol. 17, no. 1, pp.1-11.
https://search.emarefa.net/detail/BIM-682749
Modern Language Association (MLA)
al-Ubaydi, Dahlia Abd al-Hadi. Permeability prediction in one of Iraqi carbonate reservoir using hydraulic flow units and neural networks. Iraqi Journal of Chemical and Petroleum Engineering Vol. 17, no. 1 (Mar. 2016), pp.1-11.
https://search.emarefa.net/detail/BIM-682749
American Medical Association (AMA)
al-Ubaydi, Dahlia Abd al-Hadi. Permeability prediction in one of Iraqi carbonate reservoir using hydraulic flow units and neural networks. Iraqi Journal of Chemical and Petroleum Engineering. 2016. Vol. 17, no. 1, pp.1-11.
https://search.emarefa.net/detail/BIM-682749
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 10-11
Record ID
BIM-682749