1. College of Physics and Petroleum Resources, Yangtze University, Wuhan 430100, China 2. Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China 3. CNOOC Research Institute, Beijing 100027, China
Coalbed methane content is one of the key parameters in coal seam exploration and development research. Due to the influence of many factors on coalbed methane content, it is very important to predict coalbed methane content effectively. In this paper, slope correlation degree method and random forest algorithm are combined to predict coalbed methane content based on geophysical logging data. Firstly, the improved slope correlation degree method is used to obtain the favorable geophysical logging curves for CBM content prediction, and then the cross validation method is used to explore the appropriate number of random forest decision trees, and the random forest algorithm is used to predict the coalbed methane content for the logging curve sequence with positive correlation. With the No.3 seam in Shizhuang north area of Qinshui coalfield as an example, the block was evaluated and predicted with the results compared with the results of multiple regression model, and the anti-interference ability of the model was studied and analyzed. The results show that the application of slope correlation method to analyzing and calculating the geophysical logging curve and coalbed methane content can accurately and effectively find the logging curve that can be used to predict the content of coalbed methane, the model trained by random forest algorithm is accurate in predicting the content of coalbed methane in the non-gangue section, and the calculation result has high reliability, but the prediction ability is weak in the gangue section. The results obtained by the authors are of guiding significance to the exploration and development of coalbed methane and have practical application value.
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