%0 Journal Article
%T Research on Influencing Factors and Prediction Methods of Shale Gas Content Based on Machine Learning Algorithm
%A Fanjing Mao
%J Open Access Library Journal
%V 10
%N 4
%P 1-16
%@ 2333-9721
%D 2023
%I Open Access Library
%R 10.4236/oalib.1109963
%X In order to serve the research of shale gas reservoir gas content and improve the prediction effect of shale gas reservoir gas content, this paper compares the factors influencing the gas content of shale gas reservoir and general prediction methods, and carries out the analysis of the factors influencing the gas content of shale gas and the response of sensitive logs in M1 well area, determines the main controlling factors of shale gas content, and establishes the prediction model of shale gas content in the well area on this basis. In order to improve the prediction accuracy, the CatBoost algorithm was introduced to build a shale gas content prediction model in the study area and compared with the measured gas content for verification; meanwhile, to verify the applicability of the model, the log data of the neighboring well F1 in the M1 well area were imported into the model to calculate its gas content and compared with its measured gas content for verification. The results show that the main influencing factors of shale gas content in M1 well block are total organic carbon content and pore specific surface area, etc. In the conventional shale gas content prediction model, adsorbed gas and free gas are calculated separately, and the summed gas content is larger than the measured gas content; the highest accuracy of multiple regression analysis is 0.702. The accuracy of the shale gas content prediction model established by applying CatBoost algorithm with well logging and testing data as input features and corresponding measured gas content as output labels is 0.986, which is better than the conventional algorithm; the model also has a higher accuracy in predicting the shale gas content of the neighboring well F1 in M1 well area.
%K Shale Gas Content
%K Influencing Factors
%K Prediction Method
%K CatBoost Algorithm
%K M1 Well
%U http://www.oalib.com/paper/6792270