Abstract

Deterministic pre-stack Seismic inversion brings elastic properties which depend on the rock and fluid properties. However, this lacks resolution (band limited seismic) and issue of non-uniqueness remains unsolved. The stochastic seismic inversion technique integrates the fine vertical sampling of the log data with the dense areal sampling of the seismic data to create detailed, high resolution elastic property (elastic impedance, density or velocity) models utilizing geostatistical techniques. Multiple solutions address the issue of non-uniqueness and highlight the uncertainty associated with inversion. In the next step, reservoir properties are computed/inverted from elastic attributes. In the present study, a deep water gas field in Pliocene sands of KG basin was taken up to bring out seismic-guided static reservoir model. The Vp/Vs volume, realized from the stochastic inversion. that represents the most probable hydrocarbon sand facies is used for porosity prediction using Probabilistic Neural Network (PNN). Subsequently, the porosity cube is input to Petrel (Schlumberger) for static reservoir modelling.