用地球物理测井资料预测煤层气含量——基于斜率关联度—随机森林方法的工作案例

doi:10.11720/wtyht.2021.1508

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Abstract

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.

Key words： coalbed methane content slope correlation method logging curve random forest geophysical logging data

Received: 08 November 2020 Published: 01 March 2021

ZTFLH:

P631

Corresponding Authors: ZHANG Zhan-Song E-mail: 87942024@qq.com;Zhangzhs@yangtzeu.edu.cn

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	Articles by authors
	Jian-Hong GUO
	Zhan-Song ZHANG
	Chao-Mo ZHANG
	Xue-Qing ZHOU
	Hang XIAO
	Rui-Bao QIN
	Jie YU

Cite this article:

Jian-Hong GUO,Zhan-Song ZHANG,Chao-Mo ZHANG, et al. The exploration of predicting CBM content by geophysical logging data: A case study based on slope correlation random forest method[J]. Geophysical and Geochemical Exploration, 2021, 45(1): 18-28.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2021.1508 OR https://www.wutanyuhuatan.com/EN/Y2021/V45/I1/18

Relationship between coalbed methane content and logging parameters

Logging response range of No.3 Coal Seam

Calculation sample of slope correlation degree of No.3 Coal Seam

Calculation results of slope correlation degree of No.3 coal seam

Results of random forest out of bag error before and after slope correlation calculation

Cross validation to explore decision tree range results

Out of bag error when the number of decision trees is 500

Slope correlation degree-prediction of coalbed methane content by random forest

Prediction results of No.3 coal seam test set

Prediction results of No.3 coalbed methane content in A7 well

Response and experimental value analysis of No.3 coal seam in well A3

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