Hybrid Global Stochastic and Bayesian Linearized Acoustic Seismic Inversion Methodology

Seismic inversion is an important technique for reservoir modeling and characterization due to its potential in inferring the spatial distribution of the subsurface elastic properties of interest. Two of the most common seismic inversion methodologies within the oil and gas industry are iterative geostatistical seismic inversion and Bayesian linearized seismic inversion. Although the first technique is able to explore the uncertainty space related with the inverse solution in a more comprehensive way, it is also very computationally expensive compared with the Bayesian linearized approach. In this paper, we introduce a novel hybrid seismic inversion procedure that takes advantage of both the frameworks: an iterative geostatistical seismic inversion methodology is started from an initial guess model provided by a Bayesian inversion solution. Also, we propose a new approach to model the uncertainty of the retrieved inverse solution by means of kernel density estimation. The proposed approach is implemented in two different real data sets with different signal-to-noise ratios. The results show the robustness of the hybrid inverse methodology and the usefulness of modeling the uncertainty of the retrieved inverse solution.