Estimation of reservoir properties from seismic data through a Markov Chain Monte Carlo-AVA inversion algorithm. Issue 5 (1st October 2018) - Aleardi, Mattia; Ciabarri, Fabio; Mazzotti, Alfredo
Abstract : We formulate the amplitude versus angle (AVA) inversion in terms of a Markov Chain Monte Carlo (MCMC) algorithm and apply it for reservoir characterisation and litho-fluid facies prediction in offshore Nile Delta. A linear empirical rock physics model is used to link the petrophysical properties (porosity, water saturation and shaliness) to the elastic attributes (P-wave velocity, S-wave velocity and density), whereas the exact Zoeppritz equations are used to convert the elastic properties into AVA responses. The exact Zoeppritz equations allow us to take advantage of the long offset seismic acquisition and thus to consider a wide range of incidence angles in the inversion. The proposed algorithm reliably estimates the non-uniqueness of the solution that is the uncertainties affecting the estimated subsurface characteristics (both in terms of litho-fluid facies and petrophysical properties), taking into consideration the uncertainties in the prior information, the uncertainties in the estimated rock-physics model and the errors affecting the observed AVA responses. A blind test, based on available well log information, demonstrates the applicability of the proposed method and the reliability of the results. In addition, comparisons between the results provided by the implemented MCMC algorithm with those yielded by a linear AVA inversion and an analytical approach to facies prediction, show the benefits introduced by wide-angle reflections in better constraining the inverted parameters and in attenuating the noise in the predicted subsurface models. Abstract : We formulate the amplitude versus angle (AVA) inversion in terms of a Markov Chain Monte Carlo algorithm and apply it for reservoir characterisation and litho-fluid facies prediction in offshore Nile Delta. A blind test, based on available well log information, demonstrates the applicability of the proposed method and the reliability of the results.