Seismic Inversion of Reservoir Porosity with Neural Network Technology-A Case Study in Central Iraq

Abstract

Understanding the interwell distribution of the reservoir porosity is of great importance for well deployment to improve EOR. Seismic inversion with seismic and logging data is an efficient method to obtain the reservoir porosity. This study aims to demonstrate the spatial distribution of the reservoir porosity in an important formation in Central Iraq. 3D seismic attributes and logging data were combined to invert the reservoir porosity through neural network technology. The migrated 3D seismic volume, inverted P-wave impedance volume, seismic attributes and the logging data of 10 wells, were employed for training neural networks. Based on the training network, we generated the 3D porosity volume. To verify the accuracy of the inverted result, the inverted porosity were compared with those from the logging data of other 5 wells. Data slices were extracted with seismic horizons to show the lateral distribution of the reservoir porosity. The validation error shows the best multi-attribute pair is the pair of square root of P-wave impedance, quadrature trace, and instantaneous frequency. Neural network was trained with the three attribute pair. Analysis of the correlations between the predicted and the logging porosity showed the correlations from neural network training were higher than those achieved with multi-attribute regression. The porosity from the logging data of the 5 wells, which were not evolved in neural network training, coincided well with those from the inverted 3D porosity volume. That verified the accuracy of the inverted porosity volume from neural network inversion. Vertical sections and lateral slices of the inverted porosity volume were extracted to demonstrate the vertical and lateral distributions of the porosity, respectively. Data slices showed that the porosity were higher in the north and south area, and lower in the middle area. The study shows the porosity inverted from neural network technology is more reliable than that from muti-attribute regression. In addition, through this study, we demonstrate the porosity distribution in the project area. The new knowledge of the spatial distribution of reservoir porosity provides important guidance to the well deployment in the oilfield.