In field development and production, a reservoir model is a key element in the successful performance of an oil and gas field. Well logs and core data have high vertical resolution whereas seismic data has poorer resolution vertically. However, 3D seismic data has very high horizontal resolution. We will take this into account while proposing the new methodology.
Depending on wells only we can have a hi-res model far higher than what we actually need. However, in between the wells any meaningful extrapolation is flawed. Using geo-statistics through kriging algorithm and variography leads to non-geologic reservoir models. One particular process which is highly undesirable is to throw any valid well info and upscale it such as to fit the seismic. This results in a model that is inherently flawed. The important aspect of the new methodology is to follow up the seismic stochastic inversion together with a suitable rock physics modelling to achieve high resolution (reservoir scale) of different litho-facies discrimination.
In this study first a robust rock physics model is performed for not only shear velocity log prediction at not having shear log wells but also to more appropriate litho-facies differentiation at well locations using seismic elastic properties (AI vs. Vp/Vs). Afterwards, seismic pre-stack stochastic inversion is carried out to populate different types of litho-facies between the wells. Eventually, the distribution map of pay zone facies is resulted.