Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.3997/2214-4609.201900028.

Downloads

Download Preprint

Supplementary Files
Authors

Jesper Sören Dramsch , Gustavo Corte, Hamed Amini, Mikael Lüthje, Colin MacBeth

Abstract

In this work we present a deep neural network inversion on map-based 4D seismic data for pressure and saturation. We present a novel neural network architecture that trains on synthetic data and provides insights into observed field seismic. The network explicitly includes AVO gradient calculation within the network as physical knowledge to stabilize pressure and saturation changes separation. We apply the method to Schiehallion field data and go on to compare the results to Bayesian inversion results. Despite not using convolutional neural networks for spatial information, we produce maps with good signal to noise ratio and coherency.

DOI

https://doi.org/10.31223/osf.io/zytp2

Subjects

Applied Statistics, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics, Statistics and Probability

Keywords

Deep learning, Geophysics, Seismic Inversion, North Sea, Neural Network, 4D, 4D seismic, time-lapse seismic, pressure, reservoir, saturation

Dates

Published: 2019-02-21 08:28

Last Updated: 2019-02-23 07:16

Older Versions
License

CC BY Attribution 4.0 International

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.