Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction

Xianlin Ma (), Long Zhang, Jie Zhan () and Shilong Chang
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Xianlin Ma: College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Long Zhang: College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Jie Zhan: College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Shilong Chang: College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China

Mathematics, 2024, vol. 12, issue 24, 1-16

Abstract: Effective production prediction is vital for optimizing energy resource management, designing efficient extraction strategies, minimizing operational risks, and informing strategic investment decisions within the energy sector. This paper introduces a Dual-Stage Attention Temporal Convolutional Network (DA-TCN) model to enhance the accuracy and efficiency of gas production forecasting, particularly for wells in tight sandstone reservoirs. The DA-TCN architecture integrates feature and temporal attention mechanisms within the Temporal Convolutional Network (TCN) framework, improving the model’s ability to capture complex temporal dependencies and emphasize significant features, resulting in robust forecasting performance across multiple time horizons. Application of the DA-TCN model to gas production data from two wells in Block T of the Sulige gas field in China demonstrated a 19% improvement in RMSE and a 21% improvement in MAPE compared to traditional TCN methods for long-term forecasts. These findings confirm that dual-stage attention not only increases predictive accuracy but also enhances forecast stability over short-, medium-, and long-term horizons. By enabling more reliable production forecasting, the DA-TCN model reduces operational uncertainties, optimizes resource allocation, and supports cost-effective management of unconventional gas resources. Leveraging existing knowledge, this scalable and data-efficient approach represents a significant advancement in gas production forecasting, delivering tangible economic benefits for the energy industry.

Keywords: attention mechanism; production prediction; tight gas; time convolutional networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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