## 1 Introduction  
Inferring petrophysical properties from seismic data is a key step in reservoir characterization [1; 2]. The 3D information, in conjunction with well-log data, provides crucial information for enhanced field development and exploration programs, as well as potential CO\({}_{2}\) sequestration development. However, extracting petrophysical properties from seismic data is a complex inverse problem that integrates mathematical methods with physical relations like petrophysics, wave propagation, and rock physics [3]. Point-wise estimates of density (\(\rho\)), P-wave velocities (\(V_{p}\)), S-wave velocities (\(V_{s}\)), and impedances can be obtained through seismic inversion [4]. Nevertheless, uncertainty stems from the fact that many combinations of shear and acoustic impedance can result in equivalent rock properties. One approach to minimizing uncertainty or non-uniqueness is integrating rock physics and petrophysics models with seismic inversion results to estimate a unique 3D petrophysical property model [5]. Several researchers have attempted to estimate or enhance petrophysical properties through seismic inversion. In their works, [6; 7; 8] investigated the integrated approach of combining seismic inversion and attribute analysis, emphasizing their significance in reservoir characterization and the prediction of petrophysical properties. Ref. [9] is a review that explores the recent advancements in seismic reservoir characterization, with a focus on how refined seismic inversion techniques contribute to increased recovery factors. Ref. [10] delved into the application of machine-learning techniques to predict petrophysical properties from seismic data, providing valuable insights into the integration of data-driven approaches. Nevertheless, it is essential to acknowledge that, over time, there has been a continuous progression in research and analysis to enhance the prediction of petrophysical parameters through seismic inversion. Recent research has demonstrated the effectiveness of neural networks and other machine-learning techniques in property estimation [11]. Ref. [12] is an evaluation of machine-learning methods for supervised lithology classification using geophysical data and shows that RandomForest and multilayer perception (MLP) yielded the highest accuracy. Also, other ML methods with better accuracy were applied to the classification of offshore wells. Furthermore, ML models have been applied to conventional well-log data to identify the shale lithofacies [13], deep convolutional neural network for seismic horizon tracking [14], and neural networks for the interpretation of 3D seismic structural data that aim to identify potential well site [15] models. These methods offer valuable support in mitigating risks by facilitating the development of precise subsurface models through efficient workflows [16]. It is safe to conclude that the ML methods have been applied to a wide range of geophysical data, and the level of accuracy has been proven to be replicable to newly generated data. The application of these techniques proves to be suitable for enhancing existing petrophysical and rock physics workflows, thereby leading to improved result accuracy. While machine learning (ML) has shown promise in predicting petrophysical parameters, some of the major limitations and challenges associated with its application are the contexts of data quality and quantity. ML algorithms heavily rely on the quality and quantity of the training data. Limited or poor-quality data can lead to biased or inaccurate predictions. In this study, our objective is to contribute to this evolving research landscape by improving the accuracy and efficiency of predicting petrophysical parameters from seismic data by performing detailed rock physics diagnostics of well-log data (training data set) and also introduce a more efficient workflow to enhance our prediction. The motivation for our study stems from the fact that petrophysical analyses from well logs are carried out sequentially.