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
樊冬艳 1杨灿 2孙海 1姚军 1张磊 1付帅师 1罗飞2
作者信息
- 1. 深层油气全国重点实验室(中国石油大学(华东)),山东青岛 266580;中国石油大学(华东)石油工程学院,山东青岛 266580
- 2. 深层油气全国重点实验室(中国石油大学(华东)),山东青岛 266580
- 折叠
摘要
页岩气井单变量产量预测存在较强的不确定性,而现场生产动态数据同时包括多个相关指标,针对如何选取合理的多变量数据对页岩气井产量进行预测,在保证计算效率的情况下提高预测精度.页岩气井的生产动态数据集包括日产气量、日产水量、套压、油压、油嘴直径、开井时间和温度等,采用欧式距离和动态时间弯曲距离对生产动态数据时间序列进行相似性度量,依据与日产气量的相关度,把数据分为强相关时间序列和弱相关时间序列;其次,基于卷积神经网络、循环神经网络、长短期记忆网络和门控神经网络分别对全时间序列、强相关序列、弱相关序列和单变量序列进行页岩气井产量预测;最后,以平均绝对误差、均方根误差和决定系数作为评价指标,得到不同序列的误差由小到大排序为强相关序列、全时间序列、弱相关序列、单变量序列,优选的机器学习方法为门控神经网络和长短期记忆网络.结果表明,采用机器学习方法结合页岩气井强相关性序列(日产气量、套压、油压、日产水量)能有效降低预测误差,提高页岩气井产量预测效果.
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引用本文复制引用
基金项目
山东省自然科学基金(ZR2022JQ23)
国家自然科学基金优秀青年科学基金(52122402)
国家自然科学基金重大项目(42090024)
出版年
2024
国家科技期刊平台
NSTL
万方数据