Machine learning-based porosity estimation from spectral decomposed seismic data
This work addresses porosity estimation for reservoir characterization in geophysics, offering an incremental improvement over existing methods by leveraging decomposed seismic data.
The authors tackled the challenge of estimating porosity from seismic data by proposing a machine learning workflow using ResUNet++ on frequency-decomposed seismic data, achieving an R2 score above 0.9 in training and validation and robust performance with up to 5% noise.
Estimating porosity models via seismic data is challenging due to the signal noise and insufficient resolution of seismic data. Although impedance inversion is often used by combining with well logs, several hurdles remain to retrieve sub-seismic scale porosity. As an alternative, we propose a machine learning-based workflow to convert seismic data to porosity models. A ResUNet++ based workflow is designed to take three seismic data in different frequencies (i.e., decomposed seismic data) and estimate their corresponding porosity model. The workflow is successfully demonstrated in the 3D channelized reservoir to estimate the porosity model with more than 0.9 in R2 score for training and validating data. Moreover, the application is extended for a stress test by adding signal noise to the seismic data, and the workflow results show a robust estimation even with 5\% of noise. Another two ResUNet++ are trained to take either the lowest or highest resolution seismic data only to estimate the porosity model, but they show under- and over-fitting results, supporting the importance of using decomposed seismic data in porosity estimation.