Seismic Prediction of Porosity in the Norne Field: Utilizing Support Vector Regression and Empirical Models Driven by Bayesian Linearized Inversion

Seismic Prediction of Porosity in the Norne Field: Utilizing Support Vector Regression and Empirical Models Driven by Bayesian Linearized Inversion

This work aims to improve the characterization of petrophysical properties by accurately estimating subsurface porosity using seismic and well data. The study includes Bayesian Linearized Inversion to obtain elastic parameters (e.g., compressional e shear wave velocities and densities). This reduces...

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Bibliographic Details
Main Authors: Jorge A. Teruya Monroe, Jose J. S. de Figueiredo, Carlos E. S. Amanajas
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
Bayesian linearized inversion
porosity
machine learning
Online Access:https://www.mdpi.com/2076-3417/15/2/616
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