Integrated Analysis of Well Logs and Seismic Data for Reservoir Characterization to Estimate Hydrocarbon

The Main objective of oil industry worldwide is determination of accurate reservoir model. These models make an increased percentage of the world’s hydrocarbon reserves. The model requires complete information of subsurface properties such as porosity, permeability, etc. But the fundamental challenges for geologists and geophysicists to predict these properties are reservoir specificity and heterogeneity which affects reservoir performance and their well productivity. Moreover, nonlinear multivariable regression technique like Probabilistic Neural Network has been utilizes to correlate statistically the seismic attribute to achieve high correlation coefficients when cross-plotted with reservoir properties. It results in better (r2 = 0.82) correlation coefficient than linear regression model showed (r2= 0.74). The Issue is better seismic-well tie to generate synthetic seismic traces and their correlation between predicted and the true seismic trace. Therefore, we can propose to generate pseudo porosity log from the 3-D seismic volume using polynomial neural network, helps in better integration between seismic attribute and well logs to improve the reservoir characterization by providing petrophysical properties away from well controls. The proposed model tries to achieve high attribute correlation which improves the reservoir characterization lead in estimating hydrocarbon reserves. This model also assists oil and gas companies to obtain higher drilling success.