## 1 Introduction  
Reservoir characterization is an important process in the oil and gas industry, as it provides valuable insights into the properties of subsurface reservoirs, which are essential for optimizing oil and gas operations. Especially in offshore fields, reservoir characterization can be challenging when there is a lack of well data or when the available data are unevenly distributed. The lack of data or unevenly spatially distributed data can lead to significant uncertainties in the reservoir model and reduced accuracy in predictions of reservoir properties. Machine learning (ML) algorithms, such as neural networks (NN), can be trained to recognize the spatial relationships between data points and make predictions considering the reservoir property's spatial variability. Machine learning has shown great potential in improving the accuracy and efficiency of elastic parameter definition for oil and gas reservoirs. [PERSON] et al. (2001) [1] presented a new method for predicting well-log properties from seismic data using a probabilistic neural network. They cross-validated results systematically using different blind well tests for each input well to ensure minimum validation error. [PERSON] and [PERSON] (2007) [2] applied backpropagation NN to reservoir porosity prediction of the Benicanci field, Croatia. NN was trained with three seismic attributes and 14 wells. [PERSON] et al. (2009) [3] trained several NN for the lithology and saturation predictions of the Upper Pannonian and Lower Pontian sediments in the Klostar field, Croatia. Prediction results gave excellent correlation to the input data. Numerous artificial neural network (ANN) models based on artificial intelligence can be applied to reservoir parameter prediction. [PERSON] et al. (2020) [4] summarized published research on ANN for reservoir porosity prediction. In recent years, several studies have investigated the application of machine learning in elastic parameter estimation. [PERSON] and [PERSON] (2018) [5] presented a workflow for the distribution of reservoir density based on rock physics relationships. They created pseudo-wells and synthetic seismic gathers, which were used for building deep neural networks (DNN). [PERSON] et al. (2020) [6] presented hybrid theory-guided data science (TODS)-based methods for reservoir characterization. The method involves simulating numerous rock property logs based on well data statistics, followed by using rock physics theory to model the corresponding elastic response and generate synthetic seismic data through AVO modeling. A deep neural network is then trained using the simulated logs and synthetic data. Finally, the trained neural network is used to analyze real seismic data and create maps of reservoir properties. Including seismic data in machine learning workflow allows the simultaneous prediction of synthetic well logs from seismic data sets and does not require the intervention of an interpreter [7]. Neural networks can be used for seismic waveform inversion where velocity models have a vertical resolution comparable with tomography and low-frequency FWI, as presented in [8]. [PERSON] et al. (2019) [9] described an end-to-end deep neural network for seismic inversion, which removed dependency on wavelet estimation. [PERSON], et al. (2023) [10] presented two neural network models, the PorNet model and the BlstmNet model, to predict porosity directly from pre-stack seismic data. They tested these NN models on synthetic seismic gathers and compared them to the test results. This paper is focused on observing the elastic reservoir properties of the North Adriatic gas reservoir. Different scenarios of reservoir porosity ranges and thicknesses were created by varying the pseudo-well data, following the methodology suggested by [5; 6]. Synthetic pre-stack seismic data were generated, stacked, and used in the seismic inversion process. An extensive library of input data was then utilized to train a Deep Forward Neural Network (DFNN) for porosity prediction.