• 方法理论 •

### 基于随机森林算法的低煤阶煤层气开发选区预测

1. 1.西安科技大学地质与环境学院,陕西 西安 710054
2.煤与煤层气共采技术国家重点实验室,山西 晋城 048000
3.陕西煤层气开发利用有限公司,陕西 西安 710065
4.自然资源部煤炭资源勘查与综合利用重点实验室,陕西 西安 710021
• 收稿日期:2022-04-12 出版日期:2022-08-26 发布日期:2022-09-02
• 通讯作者: 王丽雅 E-mail:cyxust@126.com;1308346025@qq.com
• 作者简介:陈跃（1988—）,男,博士,副教授,从事非常规天然气地质研究。地址：陕西省西安市雁塔中路58号西安科技大学,邮政编码：710054。E-mail： cyxust@126.com
• 基金资助:
国家自然科学基金项目“润湿性制约下低阶煤不同煤岩组分甲烷解吸机制”(41902175);山西省揭榜招标项目“基于气藏工程的煤层气井人工智能排采技术与示范”(20201101002);中国博士后科学基金项目“低煤阶镜煤与暗煤润湿性差异及对甲烷解吸的影响”(2019M653873XB)

### Prediction of favorable areas for low-rank coalbed methane based on Random Forest algorithm

CHEN Yue1(),WANG Liya1(),LI Guofu2,ZHANG Lin3,YANG Fu4,MA Zhuoyuan1,GAO Zheng1

1. 1. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, Shaanxi 710054, China
2. Key Laboratory of coal and coalbed methane co-mining technology, Jincheng, Shanxi 048000, China
3. Shaanxi Coalbed Methane Development Corp.Ltd., Xi’an, Shaanxi 710065, China
4. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, Shaanxi 710021, China
• Received:2022-04-12 Online:2022-08-26 Published:2022-09-02
• Contact: WANG Liya E-mail:cyxust@126.com;1308346025@qq.com

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.

• P618.11