Deep learning predictions on a new dataset: Natural gas production and liquid level detection

Dong Wang, Jian Lian, Chengjiang Li and Yanlei Wang

PLOS ONE, 2025, vol. 20, issue 10, 1-23

Abstract: In the energy sector, accurate forecasting of natural gas production and liquid level detection is crucial for efficient resource management and operational planning. This study proposes an integrated deep learning model by incorporating bidirectional long short-term memory and Informer, for predicting these critical parameters. The bidirectional long short-term memory model, a type of recurrent neural network, is renowned for its ability to capture temporal dependencies in sequential data, making it a strong candidate for time series forecasting. On the other hand, Informer, a recent advancement in the field, offers an innovative self-attention mechanism that can handle long-term dependencies with reduced computational complexity. In addition, these models are implemented by using a comprehensive dataset of natural gas production and liquid level detection, applying rigorous preprocessing and feature engineering techniques to enhance model performance. The proposed deep learning models are evaluated on the dataset comparing with the state-of-the-art algorithms. Experimental results demonstrate the effectiveness of both models for gas production and liquid level detection, simultaneously. This study contributes to the body of knowledge by providing insights into the application of advanced deep learning techniques in the energy sector and offers a benchmark for future research in this domain.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0333905 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 33905&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0333905

DOI: 10.1371/journal.pone.0333905

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().