3D geological modeling – integration of static subsurface data

3D static modeling provides for better understanding of the geological conditions in the area of interest,
supporting the client with the desired foundation to be used in production simulation, well design and risk assessment.
We propose two main stages of the static modeling process:


  • Structural modeling

    Fundamental modeling condition is a structural model – a tight geocellular construction, created on the basis of seismic interpretation results.

The resulting geocells are further used to populate parameters using case-dependent approach. Structural models, generated with the latest technology solutions,
can be built even in the most complex geological settings. Additionally, they allow to analyse rock properties on a scale, greater than by utilizing a single reservoir model only.



  • Property modeling

The subsurface representation with the respect to the geological concept is modeled using dedicated geostatistical algorithms, honouring all data trends. Because the distribution of geological heterogeneities between wells is most frequently a challenging task, we address this problem by means of quality-controlled seismic data that fill the gaps in the undrilled areas.
Our leading approach along with state-of-the-art technology, supported by years of experience, allow our specialists to extract useful information from the seismic traces. Based on advanced seismic inversion attributes, the distribution of key reservoir parameters are predicted in order to provide continuous geological information.





Key to the success

  • Extended Elastic Impedance inversion

Reservoir characterization requires knowledge of spatial distribution of lithological/saturation parameters, like clay content, porosity and fluid type. Those can be predicted by means of Extended Elastic Impedance Inversion, performed on the pre-stack seismic data. Parametrized by the well logs, the algorithm combines AVO analysis results (Gradient and Intercept) into a new sets of data, the inversion of which, can yield volumes that quantitatively estimate the distribution of reservoir parameters over the 3D seismic area. Those volumes, as being both well-calibrated and seismic-driven, offer significantly better input for subsequent analyses (like e.g. static model) than simple well data interpolation or deterministic impedance inversion.


  • Detailed, well-based EEI analysis allow to quickly estimate the required inversion parameters, as well as geostatistically map their spatial variability.


    • Our combination of commercial and proprietary software, together with unique workflow solutions allow to perform extensive tests of different inversion parameters and real-time comparison of the inverted and measured well data.


    • Different reservoir parameters can be obtained through similar fashion workflow, ensuring their compatibility and spatial distribution consistency.



  • Lithofacies prediction by PDF’s approach

The EEI results coupled with well-driven, depth-domain facies distribution allow not only to predict facies type and fluid content, but also quantify the uncertainty in such prediction.
Our workflow is based on the modeling of probability density functions (PDF’s), computed from logging data and inverted elastic attributes at well locations.




The outputs of the process are a group of litho-probability cubes and a cube of the most probable facies, which fill the unrecognized regions between the wells.
The above products can be depth converted and imported into the structural frame of the static model and further utilized to plan new exploration activity.