The impact of seismic inversion methodology on rock property prediction

Seismic inversion is a central step within the seismic reservoir characterization workflow. In seismic inversion, seismic amplitudes are inverted to predict the spatial distribution of the subsurface elastic properties. In subsequent steps of the geomodelling workflow, the inverted models are then converted into rock properties and facies. The methodology to perform the seismic inversion does have an impact on the predictions about the spatial distribution of the rock properties of interest. Each method has advantages and limitations depending on data quality, availability and the objective of the study. This work compares the application of deterministic and stochastic elastic inversion in a challenging real dataset. The deterministic approach is based on a sparse spike approximation, while the stochastic one is a global geostatistical seismic amplitude-versus-angle inversion. Both methods are applied to a real dataset acquired over a producing field located offshore Greece. Both the complex geological setting, where one target is close to seismic resolution, and data quality affect the performance of the seismic inversion methods. The results of both inversion methods are compared in terms of facies probability using Bayesian classification over the inverted elastic models and by comparing predictions to direct measurements at a blind well location. This application example shows the ability of deterministic inversion to retrieve an inverted solution with broader geological predictions which matched gross features in the wells. On the other hand, geostatistcal inversion was able to predict thin continous sand bodies, which correspond to the finer details at and below the seismic resolution of the observed data.

Automated summary

Seismic inversion is a crucial step in the seismic reservoir characterization workflow, where the choice of method can significantly impact the prediction of spatial distribution of rock properties. The performance of inversion methods is influenced by data quality and research objectives.