Permeability prediction based on reservoir zonation by a hybrid neural genetic algorithm in one of the Iranian heterogeneous oil reservoirs

Permeability is the most important parameter for precise reservoir description and modeling. Despite the advances and modification in different methods for permeability evaluation such as well testing and well logging, the most exact method is core analysis, which is expensive and time consuming. Because of the well logging data availability in most drilled wells, attempts have been made to utilize artificial neural networks for identification of the relationship, which may exist between the logging data and core permeability. In this study, a new approach based on hybrid neural genetic algorithm has been designed to predict permeability from the well logging data in one of the Iranian heterogeneous oil reservoirs. This approach is based on reservoir zonation according to geology characteristics and sorting the data in the same manner. The predicted permeability was compared to core permeability and it shows that permeability prediction based on designing separate networks for each zone is more accurately than designing single network for all of zones.

► A new approach was introduced based on a hybrid neural genetic algorithm to predict permeability. ► This approach uses reservoir zonation according to geology characteristics. ► Data classification based on reservoir zonation was done and separate networks were designed for each zone. ► The results showed zonation model significantly better performs than single design model.