在全球能源绿色转型的背景下,页岩气作为低碳能源的重要性日益凸显,但其产量受高维、非线性及非平稳性因素影响,传统预测方法存在精度不足和计算复杂度高的问题。为此,本文提出一种基于RevIN-Autoformer-FECAM的深度学习模型,用于提升...在全球能源绿色转型的背景下,页岩气作为低碳能源的重要性日益凸显,但其产量受高维、非线性及非平稳性因素影响,传统预测方法存在精度不足和计算复杂度高的问题。为此,本文提出一种基于RevIN-Autoformer-FECAM的深度学习模型,用于提升页岩气产量预测的准确性。该模型通过可逆实例归一化(RevIN)缓解时间序列的非平稳性问题,结合Autoformer的自注意力机制捕捉长周期依赖关系,并引入频率增强通道注意力机制(FECAM)优化多频特征提取。实验基于威海页岩气田三口气井的生产数据,与Informer、Transformer等主流模型对比表明,RevIN-Autoformer-FECAM在均方误差(MSE)和平均绝对误差(MAE)指标上均显著优于基线模型,尤其在长周期预测(24~60天)中表现稳定。研究结果为复杂时序数据预测提供了高效解决方案,对页岩气开发优化具有重要应用价值。Against the backdrop of the global green energy transition, shale gas has emerged as a critical low-carbon energy resource. However, its production is influenced by high-dimensional, nonlinear, and non-stationary factors, while traditional prediction methods suffer from limited accuracy and high computational complexity. To address these challenges, this paper proposes a deep learning model, RevIN-Autoformer-FECAM, to enhance the accuracy of shale gas production forecasting. The model integrates Reversible Instance Normalization (RevIN) to mitigate non-stationarity in time series, leverages the self-attention mechanism of Autoformer to capture long-term dependencies, and introduces a Frequency Enhanced Channel Attention Mechanism (FECAM) to optimize multi-frequency feature extraction. Experiments conducted on production data from three shale gas wells in the Weihai field demonstrate that RevIN-Autoformer-FECAM significantly outperforms baseline models (e.g., Informer, Transformer) in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE), particularly showing stable performance in long-term predictions (24~60 days). The research provides an efficient solution for complex time series forecasting and holds significant application value for optimizing shale gas development.展开更多
文摘在全球能源绿色转型的背景下,页岩气作为低碳能源的重要性日益凸显,但其产量受高维、非线性及非平稳性因素影响,传统预测方法存在精度不足和计算复杂度高的问题。为此,本文提出一种基于RevIN-Autoformer-FECAM的深度学习模型,用于提升页岩气产量预测的准确性。该模型通过可逆实例归一化(RevIN)缓解时间序列的非平稳性问题,结合Autoformer的自注意力机制捕捉长周期依赖关系,并引入频率增强通道注意力机制(FECAM)优化多频特征提取。实验基于威海页岩气田三口气井的生产数据,与Informer、Transformer等主流模型对比表明,RevIN-Autoformer-FECAM在均方误差(MSE)和平均绝对误差(MAE)指标上均显著优于基线模型,尤其在长周期预测(24~60天)中表现稳定。研究结果为复杂时序数据预测提供了高效解决方案,对页岩气开发优化具有重要应用价值。Against the backdrop of the global green energy transition, shale gas has emerged as a critical low-carbon energy resource. However, its production is influenced by high-dimensional, nonlinear, and non-stationary factors, while traditional prediction methods suffer from limited accuracy and high computational complexity. To address these challenges, this paper proposes a deep learning model, RevIN-Autoformer-FECAM, to enhance the accuracy of shale gas production forecasting. The model integrates Reversible Instance Normalization (RevIN) to mitigate non-stationarity in time series, leverages the self-attention mechanism of Autoformer to capture long-term dependencies, and introduces a Frequency Enhanced Channel Attention Mechanism (FECAM) to optimize multi-frequency feature extraction. Experiments conducted on production data from three shale gas wells in the Weihai field demonstrate that RevIN-Autoformer-FECAM significantly outperforms baseline models (e.g., Informer, Transformer) in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE), particularly showing stable performance in long-term predictions (24~60 days). The research provides an efficient solution for complex time series forecasting and holds significant application value for optimizing shale gas development.
