Production dynamic prediction of gas well based on particle swarm optimization and long short-term memory

Production performance prediction of gas well is an important basis for gas reservoir production planning, development scheme preparation and dynamic production system adjustment and is of extremely important guiding significance to the development of gas reservoirs. In this paper, the production performance prediction model of long short-term memory (LSTM) deep neural network was established based on the machine learning method. In addition, the super parameters of neural network model were optimized by means of the particle swarm optimization algorithm, so as to improve the prediction effect of LSTM deep neural network. It is indicated that the production performance prediction model of gas well based on particle swarm optimization and LSTM can accurately predict the production performance of gas well and automatically optimize the super parameters of neural network and the average absolute error of its prediction result is less than 10%. What’s more, it greatly simplifies the optimization process of neural network model.