In order to address the challenges posed by the unclear production decline patterns and the difficulty in predicting production for normal pressure shale gas wells

a novel production prediction approach has been developed. This approach combines shale gas well production decline models with Long Short-Term Memory（LSTM） neural network models

leveraging machine learning techniques and different decline models for improved accuracy. Firstly

Nanchuan shale gas wells are divided into two types according to the characteristics of water production. For type 1

gas and water are produced simultaneously at the early stage

then water production decreases significantly in the later stage; while for type 2

gas and water are produced simultaneously for a long time. Secondly

double logarithmic diagnostic curves and characteristic curves are used to identify the flow stages of gas wells; then seven gas production decline models are used to analyze the production variety. Finally

the error of the decline models are used as the inputs of the LSTM model

meanwhile the yield prediction under the coupling method is obtained after superposition. The results show that a type 1 gas well

Well-X1

is in the pesudo-steady flow stage

its optimal decline model is the improved hyperbolic decline model or the AKB model; a type 2 gas well

Well-X2

is in the linear flow stage

the preferred models are SEPD decline and Duong decline model. When the error of the decline model is large

the production prediction accuracy of shale gas wells is effectively improved after coupling the LSTM model but the effect is not obvious when the error of the decline model is small.