Abstract:

In China, low-rank coal and coalbed methane resources are abundant, meanwhile, as a kind of clean energy, the development and utilization of coalbed methane（CBM） can effectively alleviate the shortage of natural gas resources, but the commercial scale development is slightly insufficient, and systematic research is urgently needed. The premise of efficient CBM development is the selection of favorable areas, but the current CBM development evaluation involves certain subjective human factors, which will indirectly affect or interfere with the prediction effect. Taking the low-rank coal in the Dafosi minefield in the Binchang mining area of Huanglong Coal Field as the research object, based on the actual production data, the random forest algorithm in machine learning is used to predict the favorable area of coalbed methane in the area. The results show that: ① Pearson correlation analysis shows that the gas content, ash content, net thickness of coal seam, structural position, roof thickness, permeability, reservoir pressure and burial depth are eight mutually independent CBM output-related parameters and can be used for model establishment; ② The Random Forest algorithm divides the CBM development area into five types of areas with different degrees, of which type Ⅰ（extremely high） to Ⅱ（highly favorable） areas account for 13.88 % of the entire study area, mainly distributed in the middle of the well field. The southeast is not suitable for subsequent deployment of well locations, and there is a distribution of highly favorable areas in the west, so the well locations for subsequent development and deployment should also be considered. ③ It can be obtained from the receiver operating characteristic（ROC） curve, and the area under the ROC curve （AUC） is 0.961, indicating that the Random Forest model has high prediction accuracy and reliable results. Using machine learning algorithms for comprehensive prediction of CBM favorable areas can avoid human subjective factors in traditional algorithms, and can provide a certain theoretical reference for subsequent unconventional oil and gas development and selection.

Key words: Random Forest, development constituency forecast, coalbed methane（CBM）, low rank coal, Huanglong Coal Field