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
