Inter-well interference seriously affects the production of shale gas wells. The evaluation and prediction of well interference degree is of great significance to the efficient development of shale gas. But the existing research mainly focuses on the interference phenomenon between shale gas wells

production performance

and parameter optimization through numerical simulation. There are few studies on the quantitative evaluation and prediction of the interference degree between shale gas wells

and the selected parameters is incomplete

which makes it difficult to objectively evaluate the well interference between shale gas wells. Therefore

the machine learning method is used to comprehensively consider the geological parameters and fracturing parameters to evaluate and predict the degree of interference between wells in the shale gas reservoir. Firstly

the initial data are processed to improve the data quality. Then

based on the processed data

cluster analysis and random forest algorithm are used to evaluate and predict the interference degree of shale gas wells. The results show that the proportions of the wells with low

medium and high well interference in the shale gas reservoirs are 25.93 %

37.03 % and 37.04 %

respectively. The fracturing factors show significant influence on the well interference degree in the shale gas reservoirs. After parameters optimization

the prediction results of well interference degree reaches 92.07 %

indicating that the developed prediction model can be applied to forecast the well interference degree in shale gas reservoirs.