Application of Optimized Artificial Neural Networks for Predicting Reservoir Permeability

Permeability is one of the most important properties of reservoirs which indicate fluid flow ability in rock reservoir's pore spaces.Determining permeability in processes such as predicting real reserve, producing and developing oil reservoirs seems essential.In oil industry, the methods of core analyses, well testing and empirical correlations are usually used to measure permeability.The conventional methods of core analyses and well testing are too long and expensive .also, there data are not provided for every well. On the other hand, empirical correlations are used for special cases and are not accurate for every situations.Due to time-related and financial limitation, developing a method for measuring petro physical properties such as: permeability based on well logging data (well logging data almost for every well are provided) could be significant.An Alternative method for evaluating permeability is Artificial Intelligence Machinery learning tools. In this study, the method of data mining has been applied to calculate reservoir permeability by applying petro physical data, At first, the data had been normalized and then horizontal and vertical permeability of an Iranian reservoirs were calculated using geophysics data and the methods of multiple layer Perceptron Neural Network, PSO and GA. Comparison of these methods showed that Combination of MLP with each of PSO or GA has the best result.

Keywords:

Artificial Neural Network , Permeability , Hydrocarbon reservoir