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

Abstract

Net-to-gross ratio 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 pseudo-wells, 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, while the AVO intercept is most sensitive to the type of pore fluid. The inversion was successfully tested on pseudo-wells 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 net-to-gross ratio and the net pay-to-net ratio, the resulting net pay, and the uncertainty.