Mendeley
TY  _  JOUR
ID  -  14000315273726
TI  -  Permeability estimation using petrophysical logs and artificial intelligence methods: A case study in the Asmari reservoir of Ahvaz oil field
JO  -  Iranian Journal of  Petroleum Geology
JA  -  ES
LA  -  fa
SN  -  2251-8738
AU  -  Mohsenipour Abouzar
AU  -   
AU  -  Zahmatkesh  iman
AU  -  Veisi Iman 
AD  -  علوم زمین
AD  -  
AD  -  دانشگاه شهید چمران اهواز
AD  -  شرکت ملی مناطق نفتخیز جنوب
Y1  -  2021
PY  -  2021
VL  -  20
IS  -  
SP  -  17
EP  -  28
KW  -  permeability
KW  -   artificial neural network
KW  -   Imperialist competition algorithm
KW  -   particle swarm algorithm
KW  -   nuclear magnetic resonance log
KW  -   Asmari reservoir
DO  -  
N2  -  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calculated using two conventional methods, free fluid model (Coates) and Schlumberger model or mean T2 (SDR). Then, by constructing a simple model of artificial neural network and also combining it with Imperialist competition optimization (ANN-ICA) and particle swarm (ANN-PSO) algorithms, the permeability was estimated. Finally, the results were compared by comparing the estimated COATES permeability and SDR permeability with the actual value, and the estimation accuracy was compared in terms of total squared error and correlation coefficient. The results of this study showed an increase in the accuracy of permeability estimation using a combination of optimization algorithms with artificial neural network. The results of this method can be used as a powerful method to obtain other petrophysical parameters.
UR  - rimag.ir/en/Article/33934
L1  - rimag.ir/en/Article/Download/33934
ER  -  
