## 1 Introduction
In traditional well-log analysis, we typically determine the volume of the rock types, porosity, and fluid saturation by using a sequential approach. The process involves computing the lithology first, followed by the porosity, and finally, the saturation using measured well-log data. Our research introduces an innovative workflow that leverages machine learning to predict petrophysical parameters from seismic data sequentially. From our seismic inversion, we obtain our impedance volume (acoustic (primary wave) impedance (AI) and shear wave impedance (SI) volumes). Using the AI and SI volumes, the algorithm predicts the lithology volume. Then, it is trained to incorporate the predicted lithology to estimate the porosity. Finally, by incorporating the predicted porosity, lithology, and measured logs, the algorithm predicts the saturation volume. We compare our results with an approach that simultaneously predicts all of the parameters at once from the seismic data. This new methodology aims to enhance the efficiency and accuracy of petrophysical analysis from seismic data. The initial phase of this investigation is dedicated to constructing a comprehensive three-dimensional model encompassing key rock attributes, specifically porosity, lithology, and water saturation. Subsequently, machine learning is employed to predict the petrophysical parameters based on property measurements derived from analogous observations. In pursuit of this, we adopt the neural network approach, as implemented in [17], which establishes a relationship between input data and target properties, as highlightedby [18; 19]. To achieve this objective, we establish a rock property model that bridges the gap between petrophysical properties and seismic rock properties, building upon the work of [20]. To facilitate this process, we first conduct a visual evaluation to assess the feasibility of differentiating lithologies, porosity levels, and fluid types by cross plotting various elastic attributes. This assessment provides essential information regarding the optimal elastic property combinations, the number of litho-classes for evaluation, and the adequacy of well-log data representation. This preliminary analysis assists in defining the cut-off values for reservoir parameters, including porosity, saturation, and clay volume, within various lithofluid classes. Moreover, this work introduces an innovative workflow that employs machine learning for the prediction of petrophysical parameters from seismic data. By utilizing rock physics modeling, we identify the most effective combination of rock properties to distinguish the primary reservoir quality responses from background trends. Given the increasing interest in carbon dioxide (CO\({}_{2}\)) sequestration, the study's final phase encompasses CO\({}_{2}\) modeling to evaluate the Utsira Formation in the Volve area as a viable CO\({}_{2}\) geological storage location. This phase involves fluid substitution modeling utilizing well-log data to quantify the effects of CO\({}_{2}\) injection. Synthetic seismograms are generated for various CO\({}_{2}\) saturation levels, and Gassmann equations are applied to calculate changes in the P-wave and S-wave velocity. Synthetic stack and gather sections are created, covering a range from 0% to 100% CO\({}_{2}\) saturation in 10% increments, with the parameters adjusted at each step. This comprehensive model provides insights into the seismic response to CO\({}_{2}\) saturation and the evolution of the injection process.