Application of Optimized Artificial Neural Networks for Predicting Reservoir Permeability

Permeability is one of the most important reservoir properties, which indicates the ability of fluids to flow through the pore spaces of the rock. determining permeability in processes such as predicting real reserve, producing and developing oil reservoirs seems essential. In the oil industry, permeability is usually measured using core analysis, well testing, and empirical correlations. The conventional methods of core analysis and well testing are too time-consuming 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 situation. Due to time-related and financial limitation, developing a method for measuring petrophysical properties such as: permeability based on well logging data (well logging data are available for almost every well) could be significant. An alternative approach for evaluating permeability is the use of artificial intelligence and machine learning tools. In this study, the method of data mining has been applied to calculate reservoir permeability by applying petrophysical data, at first, the data had been normalized and then horizontal and vertical permeability of an Iranian reservoirs were calculated using geophysical data and the methods of multiple layer perceptron Neural Network, PSO and GA. The comparison of these methods showed that combining MLP with either PSO or GA yields the best results.