Deep learning predictions on a new dataset: Natural gas production and liquid level detection - Dong Wang; Jian Lian; Chengjiang Li; Yanlei Wang (PLoS ONE, 2025)
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.