Fast-Track Reservoir Characterisation Using Bayesian Classification At A Mature Gas Field: A Case Study

Seismic AVO along with rock physics technology has been widely used specially for hydrocarbon exploration for the past three decades, this technology has been proven to increase the understanding of the uncertainties and reduce the risk of dry wells. In this study we present a rock physics driven reservoir characterization workflow with special emphasis in describing the application of Bayesian classification to deliver a robust approach to understand and predict reservoir characteristics in a mature gas field. Bayesian classification is applied by combining the AI/EI inversion and probability density functions from well data to generate probable and most-likely lithology cubes. The results from Bayesian classification exhibit significantly good correlation with the well data and show good reservoir delineation away from the wells.