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肖航,张占松,郭建宏,等. 基于随机森林结合地球物理测井资料的煤体结构识别方法及应用[J]. 科学技术与工程, 2021, 21(24): 10174-10180.
Xiao Hang,Zhang Zhansong,Guo Jianhong,et al.Coal structure identification method based on random forest combined with geophysical logging data and its application[J].Science Technology and Engineering,2021,21(24):10174-10180.
基于随机森林结合地球物理测井资料的煤体结构识别方法及应用
Coal structure identification method based on random forest combined with geophysical logging data and its application
投稿时间:2020-11-18  修订日期:2021-05-21
DOI:
中文关键词:  煤层气  煤体结构  测井曲线  随机森林分类  
英文关键词:Coalbed methane  Coal structure  Logging curve  Random forest classification
基金项目:
              
作者单位
肖航 长江大学地球物理与石油资源学院
张占松 长江大学地球物理与石油资源学院
郭建宏 长江大学地球物理与石油资源学院
秦瑞宝 中海油研究总院
余杰 中海油研究总院
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中文摘要:
      在更加复杂的地质因素影响下,常规测井方法识别煤体结构准确度低,为精确识别煤体结构,研究了煤体结构测井曲线响应机理以及随机森林决策树个数的优选,从而建立煤体结构与测井曲线的随机森林分类模型进行煤体结构识别。结果表明:决策树个数为500时,随机森林分类模型效果最佳;通过袋外误差和模型对测试集样本的预测结果可知,随机森林分类模型的结果稳定且泛化性强,并且适合处理非均衡数据,预测精度较高。可见随机森林算法能有效识别煤体结构,为煤层气开发提供帮助。
英文摘要:
      Under the influence of more complex geological factors, conventional logging methods have low accuracy in identifying coal structure. In order to accurately identify coal structure, the response mechanism of coal structure logging curve and the optimization of the number of random forest decision trees are used to investigate The random forest classification model of coal structure and logging curve is used to identify coal structure. The results show that: when the number of decision trees is 500, the random forest classification model has the best effect; through the out-of-bag error and the model's prediction results on the test set samples, the results of the random forest classification model are stable and generalizable, and extremely suitable Processing unbalanced data, with high prediction accuracy. It can be seen that the random forest algorithm can effectively identify the coal structure and provide help for the development of coalbed methane.
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