LSTM with forget gates optimized by Optuna for lithofacies prediction

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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.

Downloads

Download Preprint

Authors

Yohei Nishitsuji , Jalil Nasseri

Abstract

One of major technical competitions in energy industry relates to how optimally deep-learning architectures we can design. Optimization of hyperparameters is treated as labor-intensive. However, it is important to tune the parameters especially when we deal with relatively small targets, yet high-impact consequences can be resulted. In this study, we adapt Optuna, the global optimizer, for tuning the hyperparameter of the deep-learning scheme of the extended long-short term memory with forget gates. We apply this framework for predicting lithological facies. Although the macro difference with and without Optuna is not significant in this study, our results indicate that Optuna could make large commercial impacts when targets are small yet difficult to be captured.

DOI

https://doi.org/10.31223/X53D1V

Subjects

Computational Engineering

Keywords

Deep learning, LSTM, Hyperparameter optimization, Optuna, Oil&Gas, lithofacies classification

Dates

Published: 2022-03-15 10:12

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
https://ndr.ogauthority.co.uk