## 7 Discussion  
The use of synthetic pre-stack seismic data to generate a Deep Forward Neural Network (DFNN) for porosity prediction is a significant advancement in the field of subsurface exploration. This approach allows for the creation of accurate and efficient reservoir models, which can be used to optimize production and reduce exploration risk. The success of the DFNN in accurately predicting porosity in the North Adriatic gas reservoir highlights its potential as a valuable tool in reservoir characterization. The ability to predict reservoir properties, such as porosity, is essential for optimizing production and reducing exploration risk. By accurately predicting reservoir properties, exploration, and production companies can make more informed decisions about where to drill and how to optimize production in existing wells. The comparison of porosity prediction using input data from an extensive library versus input data from only two wells provides an objective comparison of the results. This approach demonstrates the effectiveness of the DFNN in modeling reservoir properties and the potential benefits of using a large and diverse dataset in machine learning algorithms. The ANN models encounter limitations, such as overfitting and parameter selection, which can be overcome using different variations of activation functions, such as radial bias function instead of sigmoid [35]. Deep neural networks demand large datasets that are overcome with pseudo-well and synthetic seismic data. For complex reservoirs, proper initial reservoir set-up becomes a more crucial and critical step. This leads to a discussion about the sufficiency of varying only the thickness and porosity of the North Adriatic reservoir. To define more reliable input data for pseudo-wells and seismic, it is necessary to define a rock physics model that considers several parameters, such as deposition and mineralogical description. In the future, research should consider newly acquired well data as a result of the drilling campaign and perform sensitivity analysis of this research according to new data. Although this methodology gives good results in reservoir characterization, we must not ignore uncertainty factors that can affect the research results. Shallow reservoirs saturated with gas, which cause a decrease in the speed of seismic waves, have a great influence on the resolution of seismic data in deeper reservoirs. According to [36; 37], shallow deposits presented as valley infills also can cause imaging artifacts, such as velocity pull-downs or pull-ups. Special attention should be paid when integrating data from different vertical resolutions. This refers to seismic versus well data but also to well-logging data of a significantly different resolution. Lithology discrimination turned out as a necessity to properly model fluid saturation and permeability, which significantly differ for overall uniform and high effective porosity values in thin, poorly consolidated deposits. However, having five litho-fluid classes with their probabilities increased the uncertainty in the lithology model in cases of equal probability for two or more classes. Away from well, control facies probability cubes allow great facies variations. Highlighted uncertainty factors may have influenced the distribution of lithological classes between wells. The two existing wells in the field targeted the best parts of the reservoir according to the seismic DHIs (Direct Hydrocarbon Indicators). In the space between the wells, seismic data indicate the gas-saturated but overall the worst reservoir. Based on lithology classification and integration with seismic data, but also considering a depositional model and acknowledgments from the neighboring fields, it was concluded that sandy silt could represent dominant lithology in that part. However, the characterization of that lithology class is relatively uncertain and partially based on analogs since it is minimally presented at well locations.