Estimation of net-to-gross ratio and net pay from seismic amplitude-variation-with-offset using Bayesian inversion

Net -to -gross ratio (N/G) and net pay are essential properties for characterizing turbidite reservoirs. We present a Bayesian inversion that estimates the probability density distributions of the reservoir properties from the amplitude -variation -with -offset (AVO) attributes intercept and gradient, which are measured at the top of the reservoir. The method is adapted to the region -specific characteristics of the sand -shale interbedding as observed from well data. The likelihood function is estimated by a Monte Carlo simulation, which involves generating pseudowells, seismic modeling using the reflectivity method, picking the amplitudes at the top of the reservoir, and estimating the AVO intercept and gradient. In a North Sea oil field case example, the AVO gradient is most sensitive to variations in the net -to -gross ratio, whereas the AVO intercept is most sensitive to the type of pore fluid. The inversion was successfully tested on pseudowells and synthetic seismic AVO from well data. We show that the inversion can be applied to AVO maps to produce maps of the most likely estimates of the N/G and the net -pay -to -net ratio, the resulting net pay, and the uncertainty.