Research on Daily Gas Production Prediction of Single Well Based on LSTM
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.
Deep learningBP neural networkLong Short-Term MemoryProduction forecasting
NETL
NSTL
万方数据
