Simultaneous AVA Stochastic Inversion of Seismic Data

We consider an efficient AVA stochastic inversion algorithm which performs inversion of equal angle offset volumes to volumes of Vp, Vs and density. Exact solution of Zoeppritz equations is used in order to determine reflection coefficients. Using combination of Simulated Annealing and Metropolis-Hasting sampler, we generate a set of equiprobable high-resolution volumes of elastic properties which are consistent with seismic data, well measurements and reproduce detailed geological features. Stochastic realizations generating process is constrained to reproduce seismic data, well data, low-frequency elastic trends, 3D variograms for elastic parameters, estimates of probability distributions of (Vp,Vs,Den), estimates of vertical alteration of elastic properties, etc. The multi-trace approach and extensive use of a priori geological knowledge greatly reduces influence of seismic noise on the inversion results for each of the solutions obtained. Multiple grid approach is used to properly treat large-scale variograms features. For reproduction of vertical alteration of elastic properties we use multi-point statistics. The training dataset is formed from log-derived properties. This eliminates the need for building an artificial training model. Application of multi-point statistics has a greater potential for ensuring more accurate account of vertical variations of acoustic properties, for obtaining geologically valid inversion solutions and for decreasing the uncertainty degree. The seismic inversion algorithm has been efficiently parallelized. It uses a shared memory computer and inverts simultaneously different 1D models on different processors preserving spatial correlation of elastic properties. AVA stochastic inversion results are used for cascaded stochastic simulation of lithology and fluid units, for simulation of reservoir properties, uncertainty analysis. Based on Bayesian approach we can take into account of existing uncertainties, in particular, uncertainties caused by inaccuracy of the mathematical model that relates elastic parameters and reservoir properties, and uncertainties caused by non-uniqueness and inaccuracy of seismic inversion results.