Journal of Applied Geophysics
In reservoir modelling and characterization, the integration of geological information within seismic inversion is critical to efficiently obtain accurate inverted models. The existing knowledge about the subsurface geology should be gathered and used as conditioning data to generate robust a priori models. Usually, these models explain the expected background trend of the subsurface property of interest. One of the main differences between deterministic and iterative geostatistical seismic inversion methods is how the a priori knowledge about the spatial continuity pattern of the subsurface elastic property to be inferred, is used as constraint for the inversion. Deterministic approaches use explicit a priori models, often low-frequency models, to represent the geological background of the inverted properties. On the other hand, iterative global geostatistical seismic inversion methods do not include explicitly a priori models, since the spatial continuity pattern of the elastic property to be inverted is described exclusively by a variogram model. This work explores the impact of using explicit a priori models when integrated into iterative geostatistical seismic inversion methods. Three a priori models were built with different techniques and incorporated into the inversion process using two distinct methodologies: the first directly constrains the generation of elastic models, using stochastic sequential simulation with simple kriging with locally varying means; alternatively, these a priori models are also incorporated as part of a multi-objective function, accounting for both data and model deviations. All scenarios are applied to a synthetic case study, and those with the best results are then applied to a three-dimensional real dataset. The results of this work illustrate the drawbacks and advantages of using explicit a priori models in iterative geostatistical seismic inversion, highlighting the impact on the reproduction of channels under complex and heterogeneous siliciclastic geological environments and the need of reliable a priori models for accurate predictions about the subsurface. © 2019 Elsevier B.V.
Year of publication: 2019