基于LSTM的单井日产气量预测研究
Research on Daily Gas Production Prediction of Single Well Based on LSTM
周鹏 1李绍鹏 1赵明芳1
作者信息
- 1. 贵州能源产业研究院有限公司 贵州 贵阳 550025
- 折叠
摘要
产量预测是油气藏动态分析的重要内容之一,传统的BP神经网络与统计分析广泛应用于产量预测,但在预测过程中没有考虑数据在时间上的相关性.因此,提出利用长短期记忆网络(LSTM)深度学习模型进行产量预测.该方法在RNN的基础上增加了记忆功能,解决了长期依赖问题,能够通过寻找变量之间的非线性映射关系进行预测,是国内外深度学习研究中的一个热点.经实际数据检验,LSTM网络模型取得了较好的结果,可以作为一种新的方法用于油气藏产量预测.
Abstract
Production forecasting is an essential aspect of dynamic analysis in oil and gas reservoirs.Traditional methods such as BP neural networks and statistical analysis are widely used for production forecasting;however,they often overlook the temporal correlations in the data during the prediction process.Therefore,this study proposes utilizing the Long Short-Term Memory(LSTM)deep learning model for production forecasting.Building upon the foundation of Recurrent Neural Networks(RNN),this method incorporates memory functionality,addressing the challenge of long-term dependencies.The model,which explores nonlinear mapping relationships between variables,has become a focal point in deep learning research both domestically and internationally.Through empirical data verification,the LSTM network model has shown promising results and can serve as a novel approach for predicting gas production in oil and gas reservoirs.
关键词
深度学习/BP神经网络/长短期记忆网络/产量预测Key words
Deep learning/BP neural network/Long Short-Term Memory/Production forecasting引用本文复制引用
出版年
2024
NETL
NSTL
万方数据