Permeability, or flow capacity, is the ability of porous media to transmit fluid. To understand reservoir performance and in case of reservoir management and development requires accurate knowledge of permeability. The permeability of the formation is usually determined from the cores and/or well tests. It should be noted that cores and well test data are often only available from few wells in a reservoir while the logs are available from the majority of the wells. Therefore, the evaluation of permeability from well log data represents a significant technical as well as economic advantage. Many fundamental problems remain unsolved by most predictive models. This paper introduces the use of an improved neural network trained by a Back Propagation learning algorithm to provide solution for the permeability prediction from well log data. An Iranian offshore gas field is located in the Persian Gulf, has been selected as the study area in this paper. Well log data are available on substantial number of wells. Core samples are also available from a few wells. It was shown that the neural network system is the most effective method in predicting permeability from well logs.
Ahmadi, M., Yazdian, A., & Saemi, M. (2007). Permeability Prediction of the Reservoir Formation by Using Petrophysical Information. Journal of Mining Engineering, 1(2), 43-52.
MLA
M Ahmadi; A Yazdian; M Saemi. "Permeability Prediction of the Reservoir Formation by Using Petrophysical Information". Journal of Mining Engineering, 1, 2, 2007, 43-52.
HARVARD
Ahmadi, M., Yazdian, A., Saemi, M. (2007). 'Permeability Prediction of the Reservoir Formation by Using Petrophysical Information', Journal of Mining Engineering, 1(2), pp. 43-52.
VANCOUVER
Ahmadi, M., Yazdian, A., Saemi, M. Permeability Prediction of the Reservoir Formation by Using Petrophysical Information. Journal of Mining Engineering, 2007; 1(2): 43-52.
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