Estimation of NMR Total and Free Fluid Porosity from Seismic Attributes Using Intelligent Systems: A Case Study from an Iranian Carbonate Gas Reservoir

Abstract

Nuclear magnetic resonance (NMR) log is a powerful tool for exploration and development of oil and gas fields since it can be applied to evaluate reservoir and nonreservoir horizons. Total porosity and free fluid porosity are two valuable outputs of NMR log which are accessible through processing of raw logs. In the present study, an attempt has been made to create a quantitative correlation between output parameters of NMR log and seismic attributes using linear regression method and artificial intelligent systems. An integration of 3D seismic data and well log data has been done to predict parameters of NMR log from a new source of data which is spread in the entire field and accessible in the primary stages of field development. For this purpose, the best seismic attributes were selected after extraction of acoustic impedance and sample- based attributes of 3D seismic data using stepwise linear regression method. Multivariate linear regression equations and correlation coefficients with target logs were determined. Finally, three different artificial intelligence systems including probabilistic neural network (PNN), multilayer feed-forward network (MLFN) and radial basis function network (RBFN) were designed and optimized. Results of correlation coefficients between real and predicted logs and also prediction error in the blind test showed that PNN performed better than MLFN and RBFN. At the last step, PNN was used to reconstruct the 3D model of NMR total porosity and free fluid porosity in the reservoir zone of the studied carbonate gas field in the south of Iran.