Since shale gas is a valuable energy resource,effective planning for its extraction and utilization depends on precise forecasting of gas well production.Conventional models need long computation time,a wide range of ...Since shale gas is a valuable energy resource,effective planning for its extraction and utilization depends on precise forecasting of gas well production.Conventional models need long computation time,a wide range of geological and fluid data,and suffer from unstable predictions.To develop a low-cost,intelligent,and reliable forecast system for shale gas production,a hybrid Temporal Convolutional Network-Policy Gradient Informer(TCN-PgInformer)model was constructed for multivariate production prediction research.This model is based on the Informer model of its own unique self-attention mechanism,which lowers the temporal complexity of conventional self-attention technique while increasing the model's accuracy.Meanwhile,to completely avoid the gradient vanishing problem,the dilated convolutions of TCN structure are employed to extract the long-term dependency relationships.Ultimately,a policy gradient(Pg)algorithm is introduced to enhance the parameter training speed.The results indicate that the daily gas production may be accurately predicted by TCN-PgInformer model.A detailed performance comparison was carried out among TCN-PgInformer,CNN,GRU and CNN-LSTM models in the literature.The comparison demonstrates that the suggested TCN-PgInformer model outperforms existing techniques.For four different gas production stages,the MAPE/RMSE error of other models is 2-12 times higher than that of the TCN-PgInformer model,while the R^(2) accuracy of TCNPgInformer model can be as high as 1 time higher than other models.Therefore,the designed model has excellent applicability,which offers reference and guidance for shale gas development.展开更多
文摘Since shale gas is a valuable energy resource,effective planning for its extraction and utilization depends on precise forecasting of gas well production.Conventional models need long computation time,a wide range of geological and fluid data,and suffer from unstable predictions.To develop a low-cost,intelligent,and reliable forecast system for shale gas production,a hybrid Temporal Convolutional Network-Policy Gradient Informer(TCN-PgInformer)model was constructed for multivariate production prediction research.This model is based on the Informer model of its own unique self-attention mechanism,which lowers the temporal complexity of conventional self-attention technique while increasing the model's accuracy.Meanwhile,to completely avoid the gradient vanishing problem,the dilated convolutions of TCN structure are employed to extract the long-term dependency relationships.Ultimately,a policy gradient(Pg)algorithm is introduced to enhance the parameter training speed.The results indicate that the daily gas production may be accurately predicted by TCN-PgInformer model.A detailed performance comparison was carried out among TCN-PgInformer,CNN,GRU and CNN-LSTM models in the literature.The comparison demonstrates that the suggested TCN-PgInformer model outperforms existing techniques.For four different gas production stages,the MAPE/RMSE error of other models is 2-12 times higher than that of the TCN-PgInformer model,while the R^(2) accuracy of TCNPgInformer model can be as high as 1 time higher than other models.Therefore,the designed model has excellent applicability,which offers reference and guidance for shale gas development.
