Mohseni, Hassan, Esfandyari, Moosa, Habibi Asl, Elham. (1394). Application of artificial neural networks for the prediction of carbonate lithofacies, based on well log data, Sarvak Formation, Marun oil field, SW Iran. , 5(2), 111-123. doi: 10.22059/jgeope.2015.56089
Hassan Mohseni; Moosa Esfandyari; Elham Habibi Asl. "Application of artificial neural networks for the prediction of carbonate lithofacies, based on well log data, Sarvak Formation, Marun oil field, SW Iran". , 5, 2, 1394, 111-123. doi: 10.22059/jgeope.2015.56089
Mohseni, Hassan, Esfandyari, Moosa, Habibi Asl, Elham. (1394). 'Application of artificial neural networks for the prediction of carbonate lithofacies, based on well log data, Sarvak Formation, Marun oil field, SW Iran', , 5(2), pp. 111-123. doi: 10.22059/jgeope.2015.56089
Mohseni, Hassan, Esfandyari, Moosa, Habibi Asl, Elham. Application of artificial neural networks for the prediction of carbonate lithofacies, based on well log data, Sarvak Formation, Marun oil field, SW Iran. , 1394; 5(2): 111-123. doi: 10.22059/jgeope.2015.56089

Application of artificial neural networks for the prediction of carbonate lithofacies, based on well log data, Sarvak Formation, Marun oil field, SW Iran

Geopersia
مقاله 3، دوره 5، شماره 2، 2015، صفحه 111-123    اصل مقاله (812.71 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jgeope.2015.56089
نویسندگان
Hassan Mohseni* ؛ Moosa Esfandyari؛ Elham Habibi Asl
Department of Geology, Faculty of Science, Bu-Ali Sina University, Hamedan, Iran
چکیده
Lithofacies identification can provide qualitative information about rocks. It can also explain rock textures which are important
components for hydrocarbon reservoir description Sarvak Formation is an important reservoir which is being studied in the Marun oil
field, in the Dezful embayment (Zagros basin). This study establishes quantitative relationships between digital well logs data and
routine petrographic data, obtained from thin sections description. Attempts were made to predict lithofacies in 13 wells, all drilled in
the Marun oil field. Seven well logs, namely, Gamma Ray (SGR and CGR), Deep Resistivity (RD), Formation Density (RHOB),
Neutron Porosity (PHIN), Sonic log (DT), and photoelectric factor (PEF) as input data and thin section/core-derived lithofacies were
used as target data in the ANN (artificial neural network) to predict lithofacies. The results show a strong correlation between the given
data and those obtained from ANN (R²= 95%). The performance of the model has been measured by the Mean Squared Error function
which doesn't exceed 0.303. Hence, neural network techniques are recommended for those reservoirs in which facies geometry and
distribution are key factors controlling the heterogeneity and distribution of rock properties. Undoubtedly, this approach can reduce
uncertainty and save plenty of time and cost for the oil industry.
کلیدواژه‌ها
Sarvak Formation؛ Artificial Neural Networks؛ Reservoir Characterization؛ Lithofacies؛ Zagros basin
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