计算机与现代化Issue(12)：53-58,71,7.DOI:10.3969/j.issn.1006-2475.2024.12.008
基于经验小波变换的油气井产量预测模型
Oil and Gas Well Production Prediction Model Based on Empirical Wavelet Transform
摘要
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 com-bined 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 re-sults.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 veri-fies the feasibility of applying this method to actual production prediction in oil fields.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Key words
yield prediction/empirical wavelet transform/convolutional neural network/bidirectional long short-term memory network/self-attention mechanism分类
能源科技引用本文复制引用
张晓东,白广芝,李敏,李昊洋..基于经验小波变换的油气井产量预测模型[J].计算机与现代化,2024,(12):53-58,71,7.基金项目
生产性研究项目(HX20211004) （HX20211004）
国家自然科学基金资助项目(61801517) （61801517）
