中国石油大学学报（自然科学版）2024，Vol.48Issue(3)：119-126,8.DOI:10.3969/j.issn.1673-5005.2024.03.013
基于时间序列相似性与机器学习方法的页岩气井产量预测
Shale gas well production forecasting based on time sequence similarity and machine learning methods
摘要
Abstract
Production data from shale gas wells contains multiple different dynamic variables during on-site collection,and there is uncertainty for production forecasting if only a single variable is used.It is important to choose reasonable multi-vari-able data to predict the output of shale gas wells,and ensure the precision accuracy and computing efficiency.In this study,a new method was proposed.Firstly,a dynamic data set can be comprehensively collected,including daily gas rate,water rate,well pressure,oil choke size,well opening time and fluid temperature.Euclidean distance and dynamic time warping were used to perform similarity testing of the production dynamic data time sequences.Based on the correlation with daily gas rate,the production data were divided into strong related time series and weak related time sequences.Secondly,based on convolutional neural network,recurrent neural network,long and short-term memory network(LSTM)and gate recurrent u-nits(GRU),the shale gas well production was predicted for full-time sequences,strong related sequences,weak related se-quences and univariate sequences,respectively.Evaluation indicators were used to verify the methods,including average ab-solute error,root mean squared error and decision coefficient.The results indicate that the order of error from small to large for different sequences is the strong related sequence,the full time sequence,the weak related sequence,the univariate se-quence.The preferred machine learning methods are the GRU and LSTM models.The strong correlation sequence can be used to improve the accuracy and reduce errors in shale gas well forecasting.关键词
页岩气井/机器学习/相似性/时间序列/产量预测Key words
shale gas well/machine learning/similarity/time series/productivity prediction分类
石油、天然气工程引用本文复制引用
樊冬艳,杨灿,孙海,姚军,张磊,付帅师,罗飞..基于时间序列相似性与机器学习方法的页岩气井产量预测[J].中国石油大学学报（自然科学版）,2024,48(3):119-126,8.基金项目
山东省自然科学基金项目(ZR2022JQ23) （ZR2022JQ23）
国家自然科学基金优秀青年科学基金项目(52122402) （52122402）
国家自然科学基金重大项目(42090024) （42090024）
