The goal of seismic reservoir characterization is to estimate rock and fluid properties from seismic data. The solution of the seismic inverse problem is based on the forward models that describe physical relationships between lithology and fluid parameters and their seismic response. If the forward models indicate nonlinear physics relations between the variables, advanced inversion methods, such as stochastic optimization algorithms, should be adopted to predict the reservoir properties. However, these methods are generally time consuming. In this paper, we propose an inversion approach that combined linearized AVO modeling and linearized rock physics relations. The rock physics model is based on Nur's critical porosity model and Gassmann's equations and its linearization is based on first-order Taylor series approximation. The linearization is computed with respect to solid elastic moduli, solid density, fluid bulk modulus, fluid density and porosity. Indeed, the rock physics model is almost linear in these properties and the linearization of the model provides a good approximation. The combined forward model is then used in a Bayesian inversion workflow for the estimation of the above-mentioned rock and fluid properties from pre-stack seismic data and well logs. In the Bayesian inversion, we assume the prior model and the error term to be distributed as Gaussian distribution to derive the analytical solution of the Bayesian inverse problem. The reservoir properties of interest, i.e. porosity, mineral volumes, and fluid saturations, are then computed from the inversion results. The proposed method was first validated on a synthetic dataset and then applied to a field dataset with satisfactory inversion results.
Presentation Date: Tuesday, September 26, 2017
Start Time: 8:55 AM
Presentation Type: ORAL