The classification and identification of shale gas “sweet spot” involves a variety of different factors

which requires personal experience

and is usually time and resources consuming. In order to solve this problem

an efficient and effective classification and identification method for shale gas “sweet spot” based on the Random Forest method is proposed. Firstly

data from ten wells in Changning area are selected and eleven features are selected for “sweet spot” classification by the Kendall correlation. Then

the single decision tree and the Random Forest method are used for the “sweet spot” classification and identification. Finally

the results are verified and the Random Forest parameters are optimized. The experimental results show that although the prediction accuracy of a single decision tree can reach 97.7 %

it shows a trend of overfitting

and the fitting accuracy is greatly reduced by only 70.7 % after pruning. The Random Forest method avoids the disadvantage of the single decision tree method

and the prediction accuracy reaches 98 %. Moreover

the computational cost is low

which can effectively reduce the time loss and save the labor cost. As a result

the proposed Random Forest machine learning method with multi-source information is an effective shale gas “sweet spot” classification and identification method.