Stochastic inversion of Post-stack seismic data using fractal-based initial model

Abstract

Stochastic inversion has been employed to the post�stack seismic data using fractal-based initial model to enhance the resolution by integrating with high resolution well log data. A synthetic initial model of reflectivity series has been developed using statistical parameters like mean, standard deviation and the Hurst coefficient of well log data. Further, the initial model is globally optimized using Genetic algorithm with reflectivity series obtained from both seismic and well log data simultaneously. The final synthetic reflectivity series of three CDPs (1661, 1662 and 1663) adjacent to well log (at CDP 1662) show high correlation and less error, exhibiting the higher frequency components or enhanced resolution.