基于灰色系统与测井方法的煤层气含量预测及应用

doi:10.11720/wtyht.2020.1561

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Abstract

The content of coalbed methane is an important parameter in evaluating coalbed reservoir. In this paper, the gray system was applied to the coalbed logging curve, the improved slope correlation method was used to analyze the logging curve series which are sensitive to the coalbed gas content. The gray multivariate static model GM (0,N) was used to predict the coalbed methane content in the sequence of positive correlation logging curves. Taking Qinshui Coal Field as an example, the authors compared the gray multivariate static model prediction results with the results of the multiple regression model analysis, and studied and analyzed the practicability of the gray multivariate static model. The results show that the improved association analysis of gray incidence can fully develop the relationship between logging curve and coalbed methane content, and that the GM(0,N) prediction model is more accurate and more robust than the multiple regression model in that it can effectively predict the coalbed methane content curve when the sample data is relatively small. The result is reliable and has practical application value.

Key words： coalbed methane content logging curve association analysis of gray incidence GM(0,N) prediction model

Received: 02 December 2019 Published: 26 October 2020

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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
	Zhi-Ruo CHEN
	Peng-Hao ZHANG
	Xiao TANG
	Rui-bao QIN
	Jie YU

Cite this article:

Jian-Hong GUO,Zhan-Song ZHANG,Chao-Mo ZHANG, et al. Prediction and application of coalbed methane content based on gray system and logging method[J]. Geophysical and Geochemical Exploration, 2020, 44(5): 1190-1200.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2020.1561 OR https://www.wutanyuhuatan.com/EN/Y2020/V44/I5/1190

Test gas content and logging standardization parameters of coal seam 15

Relationship between gas content of coal reservoir and logging parameters

	$R 12$ 自然伽马	$R 22$ 深电阻率	$R 32$ 补偿密度	$R 42$ 声波时差
拟合优度	0.522	0.227	0.413	0.408
关联序	1	4	2	3

Linear regression correlation coefficient results

The calculation results of slope correlation degree in this paper

Predicted gas content curve of coal seam 15 in well X

Relationship between the prediction of gas content and the measurement of gas content in coal seam

The relationship between testing gas content and grey prediction gas content

GM(0,7) model predicts the deep CBM content based on different sample numbers

The relationship between the number of sample data and the average error of prediction

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