FORECASTING OIL PRODUCTION USING LSTM NETWORKS CONFINED TO DECLINE

  • Aman Zhumekeshov Suleyman Demirel University
  • Andrey Bogdanchikov Suleyman Demirel University

Abstract

Natural resources are limited and very important in our industrial life and development. Oil is considered as the black gold and it is included in hundreds of industrial fields. Therefore, forecasting future oil production performance is an important aspect for oil industry. In this study, we proposed improvements to the existing deep learning model in order to overcome limitations associated with the original model. For evaluation purpose, proposed and original deep learning models were applied on a real case oil production data. The empirical results show that the proposed adjustments to the existing deep learning model achieves better forecasting accuracy.

Author Biographies

Aman Zhumekeshov, Suleyman Demirel University
Master StudentDepartment of Computer SciencesEngineering and Natural Sciences Faculty
Andrey Bogdanchikov, Suleyman Demirel University
Dean of Engineering and Natural Sciences Faculty
Published
2020-06-17
How to Cite
ZHUMEKESHOV, Aman; BOGDANCHIKOV, Andrey. FORECASTING OIL PRODUCTION USING LSTM NETWORKS CONFINED TO DECLINE. SDU Bulletin: Natural and Technical Sciences, [S.l.], v. 52, n. 1, june 2020. Available at: <https://journals.sdu.edu.kz/index.php/nts/article/view/51>. Date accessed: 18 apr. 2025. doi: https://doi.org/10.47344/sdubnts.v52i1.51.