Comparative Analysis between Machine Learning Algorithms and Multilinear Regressions for Petro-Elastic Modeling (PEM) and Seismic Petrophysics

The primary objective of this study is to explore the application and advantages of machine learning (ML) in seismic petrophysics and reservoir characterization, with a focus on quantitative interpretation, facies prediction, and petro-elastic modelling. Traditional approaches to seismic interpretation involve complex, time-intensive workflows that often struggle to capture the heterogeneity of subsurface formations. By leveraging ML, this work aims to improve prediction accuracy, streamline workflows, and enhance the reliability of interpretations for efficient reservoir management and development. The scope includes evaluating ML techniques for seismic attribute analysis, facies classification, and the prediction of essential reservoir properties, such as porosity, lithology, and fluid saturation, within various geophysical frameworks.