###### Abstract  
Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10 th, 100 th, and 1000 th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction. shale gas; machine learning; multi-layer perceptron; long short-term memory; productivity prediction +
Footnote †: journal:forecast [10; 11]. ML is good at dealing with nonlinear problems. Usually, the calculation speed of ML is faster than numerical analysis. This requires a deeper understanding of productivity prediction in shale gas reservoirs based on machine learning algorithms. An artificial neural network is used to predict shale gas production, because it can learn the relationship between geological and fracturing reservoir parameters and gas production. Moreover, a few particular artificial neural networks are trained to describe the relationship of the production data. [PERSON] et al. [12] predicted the oil production with higher-order neural network in a reservoir in India. The network has a good forecast effect with insufficient field data available. [PERSON] et al. [13] used nonlinear autoregressive neural network to predict oil production, which shows competitive accuracy in a naturally fractured oilfield. They also found that preliminary clustering is an effective means to enhance the prediction veracity. [PERSON] et al. [14] predicted oil production with a multilayer neural network. They evaluated the algorithm using a real field dataset and found the model can predict univariate and multivariate reservoir dynamics. [PERSON] et al. [15] applied ANN to predict production using geological maps, production data, and pressure and operational constraints. [PERSON] et al. [16] compared the RNN productive prediction model and decline curve analysis of single and multiple wells. They found that LSTM models can describe the overall trend, whereas DCA can only generate smooth curves. Previous research has shown that machine learning shows efficient prediction performance for well production. However, the constraints of geological and fracturing parameters are ignored. Thus, a shale gas productivity forecasting model, which takes the historical production data with the constraints of geological and fracturing reservoir parameters, urgently needs to be established. In this study, we investigate the feasibility of production forecast based on machine learning algorithms, and use MLP and LSTM neural networks to predict shale gas production.