Abstract

The present work created a methodology to forecast impedance in the inter-well zones of the Blackfoot field, Canada, by combining Global optimization (genetic algorithm) and local optimization (pattern search) while utilizing exclusively seismic reflection data. The Blackfoot Field is an oil and gas field where a glauconitic compound incised-valley system describes a region where sediments rich in glauconite serve as reservoir rocks for fossil fuel. The algorithms are first tested on artificially generated data based on the wedge model. The actual post-stack seismic data is then converted into impedance utilizing a designed hybrid optimization approach in the Blackfoot field, Canada. Very high-resolution subsurface information may be seen in the inverted or anticipated sections, where impedance ranges from 6000 to 12000 m/s*g/cc. The results also showed a low impedance (6000-9000m/s*g/cc) sand channel anomaly in the 1040