计算机与现代化 ›› 2024, Vol. 0 ›› Issue (12): 53-58.doi: 10.3969/j.issm.1006-2475.2024.12.008

• 算法设计与分析 • 上一篇    下一篇

基于经验小波变换的油气井产量预测模型  



  

  1. (1.中国石油大学(华东)计算机科学与技术学院,山东 青岛 266580; 2.大庆油田采油工程研究院采气研究室,黑龙江 大庆 163000)
  • 出版日期:2024-12-31 发布日期:2024-12-31
  • 基金资助:
    生产性研究项目(HX20211004); 国家自然科学基金资助项目(61801517)

Oil and Gas Well Production Prediction Model Based on Empirical Wavelet Transform

  1. (1. School of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China;
    2. Gas Production Research Laboratory, Oil Production Engineering Research Institute, Daqing Oilfield, Daqing 163000, China)
  • Online:2024-12-31 Published:2024-12-31

摘要: 油气井产量预测对油气资源高效开发具有重要意义。针对间开生产等人工作业因素导致产量数据非线性强、预测难的问题,本文提出一种融合经验小波变换(EWT)和卷积双向长短期记忆网络的双通道产量预测模型。模型一部分采用EWT分解产气量数据,对分解后的子序列采用双向长短期记忆网络(BiLSTM)进行时域和频域特征提取;模型另一部分对多维历史生产数据采用一维卷积神经网络(1D-CNN)进行局部时序特征提取,然后使用BiLSTM并结合自注意力机制从1D-CNN模块的输出特征中挖掘气井生产数据的全局特征。最后,将模型的2个部分进行特征融合,生成最终预测结果。利用某气井生产后期历史数据开展实验建模分析,发现针对人工措施频繁的复杂产量序列本文方法预测结果更准确,表明了本文方法应用于油气田实际生产预测的可行性。

关键词: 产量预测, 经验小波变换, 卷积神经网络, 双向长短期记忆网络, 自注意力机制

Abstract: Oil and gas well production prediction is of great significance for efficient development of oil and gas resources. A two-channel production prediction model incorporating empirical wavelet transform (EWT) and convolutional bi-directional long and short-term memory network is proposed to address the problem of strong nonlinearity and difficulty in prediction of production data due to inter-opening production and other artificial operational factors. One part of the model uses EWT to decompose gas production data, and the decomposed subseries are extracted in the time and frequency domains using a bi-directional long and short-term memory network (BiLSTM); the other part of the model uses a one-dimensional convolutional neural network (1D-CNN) to extract local time-series features from the multidimensional historical production data, and then uses BiLSTM combined with a self-attentive mechanism to extract the output features from the 1D-CNN module output features to mine the global features of gas well production data. Finally, the features of the two parts of the model are fused to generate the final prediction results. Experimental modeling analysis is carried out using the late production history data of a gas well, and it is found that the prediction results of this method are more accurate for complex production sequences with frequent manual measures, which verifies the feasibility of applying this method to actual production prediction in oil fields.

Key words:  , yield prediction; empirical wavelet transform; convolutional neural network; bidirectional long short-term memory network; self-attention mechanism

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