Geostatistical seismic Amplitude-versus-angle inversion

Seismic inversion plays an important role in reservoir modelling and characterisation due to its potential for assessing the spatial distribution of the sub-surface petro-elastic properties. Seismic amplitude-versus-angle inversion methodologies allow to retrieve P-wave and S-wave velocities and density individually allowing a better characterisation of existing litho-fluid facies. We present an iterative geostatistical seismic amplitude-versus-angle inversion algorithm that inverts pre-stack seismic data, sorted by angle gather, directly for: density; P-wave; and S-wave velocity models. The proposed iterative geostatistical inverse procedure is based on the use of stochastic sequential simulation and co-simulation algorithms as the perturbation technique of the model parametre space; and the use of a genetic algorithm as a global optimiser to make the simulated elastic models converge from iteration to iteration. All the elastic models simulated during the iterative procedure honour the marginal prior distributions of P-wave velocity, S-wave velocity and density estimated from the available well-log data, and the corresponding joint distributions between density versus P-wave velocity and P-wave versus S-wave velocity. We successfully tested and implemented the proposed inversion procedure on a pre-stack synthetic dataset, built from a real reservoir, and on a real pre-stack seismic dataset acquired over a deep-water gas reservoir. In both cases the results show a good convergence between real and synthetic seismic and reliable high-resolution elastic sub-surface Earth models.