## 6 Seismic Data Integration in the Static Reservoir Model  
Just as there are different methods of seismic inversion, there are also different methods of implementing these results into a reservoir model. The selection of the method and its adaptation largely depends on the availability and quality of the input data, the type of the deposits, and the contained fluids. Numerous authors, for example, [PERSON] and [PERSON] (2012) [29], advocated the approach of both facies and corresponding reservoir property distribution based on seismic reservoir characterization results. That is supported by more recent studies that are generally based on robust rock physics models. [PERSON] et al. (2017) [30] documented the method based on integrated petrophysical and rock physics analysis, thus improving the results of reservoir characterization. [PERSON] et al. (2018) [31] highlighted the importance of seismic inversion together with suitable rock physics modeling to achieve high-resolution lithofacies discrimination. Having the well data set of only two wells, both were drilled in the areas of best seismic DHIs, while at the same time, seismic showing weaker DHIs in the large area without the well data did not allow for robust rock physics modeling in this analyzed case. Figure 11: Comparison of reservoir total porosity maps: (**a**) result of DFNN trained on synthetic catalog distribution; and (**b**) result of DFNN trained on input well data distribution. Figure 10: Synthetic well profiles with modeled (red) and input (black) total porosity. Generally, for clastic reservoirs, frequent spatial variations in lithology and reservoir quality make reservoir properties assessment challenging, while gas content often masks the original rock's elastic and physic properties. At the same time, gas-related DHIs in the seismic volume provide valuable information for spatial gas saturation boundaries definition in case of missing delineation and appraisal wells. In this presented case, as a part of lateral reservoir extent analysis, an automatic geobody extraction was performed on the Horizon probe from pre-stack Acoustic Impedance (Zp) volume for different AI windows. The range of AI values within a 50 ms thick probe is between 1500 and 5000 (m/s)(g/cm\({}^{3}\)). Based on integrated seismic and well data, values below 2700 are associated with good reservoir and gas-saturated lithologies (Figure 12). The purpose of seismic and well data integration and correlation is to find the geological meaning of seismic data and utilize it to propagate the rock and fluid properties in a modeled subsurface volume. The final aim is to achieve as much accuracy as possible in reservoir volume characterization with the input data set available. In the case of the analyzed reservoir, all available well logs were strongly influenced by the gas effect, and parts of seismic volume were considered to correspond to a gas-saturated reservoir. Acquired well logs and fluid saturation profile indicate more significant variations in reservoir quality than indicated from, in general, low-contrast porosity curves (Figure 13). Based on the analog data from the neighboring fields, primary routine, and special core analysis, it was assumed to be a consequence of variations in lithology. Core data for the analyzed reservoir were not available. In the previously constructed structural model, reservoir characterization workflow was adjusted to the available input data and their reliability. Seismic data, along with the well logs and analysis, were utilized to perform multi-step reservoir characterization:  
1. Neural network methodology was utilized for lithology classification with the scope to differentiate reservoir quality classes and better capture reservoir quality variations in terms of reservoir parameters (Figure 13);
2. Lithology class occurrence probability analysis, derived from seismic impedance volume, previously correlated with lithology classes;
3. Probability cubes from the previous step were used for stochastic facies modeling;
4.