## 8 Conclusions  
The seismic reservoir characterization results presented in this study highlight the importance and novelty of incorporating advanced techniques and data integration for improved reservoir property estimation. The use of pre-stack inversion allowed for the generation of 3D volumes of acoustic impedance, shear impedance, and \(VpVs\) ratio, providing valuable information about the subsurface properties. The seismic interpretation revealed good reservoir continuity between wells and identified the presence of gas-bearing channel and mouth-bar deposits, which had an impact on the impedance images. One of the key contributions of this study, the first of this type in the Adriatic Basin, is the introduction of variations of porosity and thickness, resulting in a large number of reservoir property combinations. This approach goes beyond the traditional use of the input well data and expands the range of scenarios considered. The validation of porosity predictions using a subset of wells demonstrated a high correlation, highlighting the accuracy of the method. The success of the Deep Forward Neural Network (DFNN) in accurately predicting porosity in the analyzed reservoir is a significant advancement in subsurface exploration. The comparison between predictions using an extensive library versus limited well data demonstrated the effectiveness of the DFNN and the benefits of using a diverse dataset. However, this study acknowledges the limitations of the DFNN models, such as overfitting and parameter selection, which can be addressed with variations of activation functions and larger datasets. This study also addressed the challenge of implementing seismic reservoir characterization results into a static reservoir model. Various methods were discussed, with an emphasis on facies and reservoir property distribution based on seismic data. Clastic reservoirs, such as the one analyzed in this study, are, in general, associated with significant spatial variations in lithology and reservoir quality, making the assessment of reservoir properties challenging. The incorporation of gas-related DHIs in the seismic volume provided valuable information for defining gas saturation boundaries in the absence of delineation and appraisal wells. The integration and correlation of seismic and well data aimed to extract the geological meaning of seismic data and propagate rock and fluid properties within the subsurface volume. The application of neural network methodology for litho-fluid classification allowed for better differentiation of reservoir quality classes and improved reservoir parameters capture. The lithology classes derived from the well logs classification process were correlated with seismic impedance, specifically, P-impedance, to establish quantitative interdependencies. The lithology probability analysis based on P-impedance values provided insights into both gas and water saturation and their association with different lithology classes. The resulting probability cubes formed the basis for stochastic facies modeling, which, in turn, guided the reservoir property modeling, considering porosity, permeability, and water saturation. The integration of data with different vertical resolutions, including seismic versus well data, requires careful consideration to mitigate uncertainties. Furthermore, future research should incorporate newly acquired well data to improve the reliability of the analysis and perform sensitivity analysis accordingly. The accuracy and detail of reservoir property estimation are significantly improved by implementing pseudo-well data, machine learning algorithms, and advanced seismic analysis techniques. The study demonstrated the effectiveness of the DFNN in modeling reservoir properties and highlighted the potential benefits of using a large and diverse dataset, especially in high-porosity sandstones. These results have implications for subsurface exploration of similar reservoirs, enabling the reduction in exploration risk and optimization of field development plans.