多源测井数据预测煤层工业组分和发热量模型研究

doi:10.11720/wtyht.2024.2553

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Abstract

The industrial components and calorific values of coal seams serve as an important basis for the evaluation of coal quality, and the prediction of them based on log data allows for overcoming the deficiency in the experimental analysis of coal core samples. This study collected data from digital logs and coal quality analysis at different stages (e.g., detailed survey and exploration) of a coal field in Ningxia. Based on the investigation of the coal quality and log responses, as well as statistical analysis, this study developed the methods for extracting log response characteristics, establishing sample sets, and processing data and established a deep neural network-based prediction model. Then, it confirmed the validity of the prediction model by comparing the predicted results of testing data with the results from the experimental analysis.

Key words： multi-source data geophysical logging coal industrial component calorific value prediction model

Received: 08 November 2022 Published: 26 February 2024

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	Articles by authors
	Yong-Peng YU
	Guang-Bing ZHANG
	Zi-Jun HUANG
	Jian-Bo YAN
	Jia-Wen WANG
	Yan-Cheng YANG
	Xing-Jun MAO

Cite this article:

Yong-Peng YU,Guang-Bing ZHANG,Zi-Jun HUANG, et al. A prediction model of the industrial components and calorific values of coal seams based on multi-source log data[J]. Geophysical and Geochemical Exploration, 2024, 48(1): 185-193.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2024.2553 OR https://www.wutanyuhuatan.com/EN/Y2024/V48/I1/185

The characteristic map of coal quality of mineable coal seams in the study area

The linear correlation diagram of coal quality data in the study area

The logging response characteristics of coal seams in the study area

The amplitude frequency histogram of typical logging curves in the study area

测井方法	自然伽马/API		短源距人工伽马/CPS		长源距人工伽马/CPS		三侧向视电阻率/(Ω·m)
对比内容	$最小值最大值$	$均值方差$	$最小值最大值$	$均值方差$	$最小值最大值$	$均值方差$	$最小值最大值$	$均值方差$
详查阶段	$3.97 23.32$	$10.05 2.66$	$1358419100$	$17109.20 1038.68$	$4356 10424.70$	$9169.26 680.22$	$2.70 7.19$	$4.97 0.60$
勘探阶段	$3.56 18.68$	$10.08 2.03$	$31869226$	$8094.60 733.03$	$60438679$	$7266.61 524.10$	$3.25 7.85$	$5.14 0.82$

The statistical table of amplitude characteristics of typical logging curves in the study area

The statistical table of sample quantity of sample set

The linear correlation coefficient heat diagram of normalized logging data and the industrial components and calorific value of coal in the study area

The deep neural network structure diagram

The main configuration parameters of deep neural network

The multivariate linear regression model for predicting A_d and V_daf in coal seams

The comparison diagram of prediction results of test set samples and analysis results of coal core samples

煤质参数	样品分析结果 ( $最大值 - 最小值平均值$ )	预测结果 ( $最大值 - 最小值平均值$ )	均方根误差/%	平均绝对误差/%	平均相对误差/%	备注
M_ad/%	$15.60 - 4.48 9.89$	$14.48 - 5.02 9.53$	2.14	1.62	18.34
A_d/%	$17.49 - 3.76 8.60$	$15.04 - 5.36 9.09$	3.06	2.41	26.89	分步预测
A_d/%	$17.49 - 3.76 8.60$	$19.02.21 - 4.85 8.60$	3.61	2.62	29.55
FC_d/%	$69.05 - 47.38 57.44$	$64.51 - 47.76 56.94$	3.66	2.88	5.08
V_daf/%	$39.16 - 27.14 32.92$	$36.71 - 30.68 33.39$	2.90	2.30	6.89	分步预测
V_daf/%	$39.16 - 27.14 32.92$	$39.08 - 28.29 33.40$	3.42	2.58	7.78
Q_gr,d/(MJ·kg^-1)	$31.70 - 25.91 28.96$	$30.78 - 26.78 28.73$	1.18	0.90	3.14

The list of errors in comparison between prediction results and sample analysis results in the test set

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