Sequential Generative-Supervised Strategies for Improved Multi-Step Oil Well Production Forecasting
Funding Sponsor
Information Technology Industry Development Agency
Author's Department
Computer Science & Engineering Department
Second Author's Department
Computer Science & Engineering Department
Third Author's Department
Computer Science & Engineering Department
Find in your Library
https://doi.org/10.1145/3647750.3647758
Document Type
Research Article
Publication Title
ACM International Conference Proceeding Series
Publication Date
1-26-2024
doi
10.1145/3647750.3647758
Abstract
Generative Adversarial Networks (GANs) exhibit great potential in many areas. In this paper, we explore their potential in multi-step time series forecasting. To the extent of our knowledge, this task has not been extensively researched yet, possibly due to its unique challenges when trying to model the original temporal behavior of the data. We propose a model for concrete multi-step forecasting where we mix the generative power of the unsupervised GAN loss with the deterministic prediction capabilities of supervised losses. We do this in a rather simple, sequential manner that proves to be helpful for both components of the architecture. The unsupervised component does its job by offering multiple generated predictions that follow the temporal dynamics of the time series, while the supervised component acts as a prediction selector that inspects the provided outputs and creates the most accurate one. We apply this approach in the energy sector, particularly using real industry data on oil well production, as provided to us by Raisa Energy. This learning approach leverages the generative component to provide superior results to those of the supervised counterpart. The approach also stabilizes the overall training, thereby improving the results and providing a more reliable training process.
First Page
45
Last Page
52
Recommended Citation
APA Citation
Mikhael, M.
Sakr, N.
&
Elbatt, T.
(2024). Sequential Generative-Supervised Strategies for Improved Multi-Step Oil Well Production Forecasting. ACM International Conference Proceeding Series, 45–52.
10.1145/3647750.3647758
https://fount.aucegypt.edu/faculty_journal_articles/6205
MLA Citation
Mikhael, Mina, et al.
"Sequential Generative-Supervised Strategies for Improved Multi-Step Oil Well Production Forecasting." ACM International Conference Proceeding Series, 2024, pp. 45–52.
https://fount.aucegypt.edu/faculty_journal_articles/6205
Comments
Conference Paper. Record derived from SCOPUS.