NMR Log Prediction from Seismic Attributes: Using Multiple Linear Regression and Neural Network Methods

Abstract

This study proposes a strategy to make a quantitative correlation between the NMR log-derived free fluid porosity and seismic attributes using multiple linear regression and artificial neural network. At first the well logs tied to seismic data by creating a synthetic seismogram at each well using the sonic and density logs. Then the post-stack seismic data will be inverted to acoustic impedance by applying the created synthetic seismograms. The higher resolution of well logs than seismic data problem is dissolved by three-dimensional modeling and averaging all of the seismic and logs data in each model cell. Then the cell's properties are processed as inputs and outputs for all operations. Stepwise regression is used to select the best attributes predicting the free fluid porosity. The multiple linear regression equation and its correlation at each step are shown. Finally, an artificial neural network of multi-layer neural network type with back propagation of errors is used for the prediction, and the better method between these two methods was selected to apply it to the whole volume of study. The proposed methodology was applied to the South Pars Gas Field in the Persian Gulf Basin. The seismic attributes were extracted from three separated seismic parts. The petrophysical logs from four wells in these seismic cubes were used for generating and evaluating the reliability of the neural network.