Prediction of coal seam gas content based on ABC-BP model

被引：0

作者：

Zang Z. ^{[1
]}

Wu H. ^{[1
]}

Zhang P. ^{[1
]}

Dong S. ^{[2
]}

机构：

[1] School of Earth and Environment, Anhui University of Science and Technology, Huainan

[2] School of Resources and Earth Sciences, China University of Mining and Technology, Xuzhou

来源：

Meitiandizhi Yu Kantan/Coal Geology and Exploration | 2021年 / 49卷 / 02期

关键词：

Artificial bee colony algorithm; BP neural network; Coalbed methane reservoir; Gas content; Seismic attribute optimization;

D O I：

10.3969/j.issn.1001-1986.2021.02.019

中图分类号：

学科分类号：

摘要：

It is valuable to exploit the coalbed-methane which is rich in our country. The prediction of gas content in coalbed methane reservoir is a key step in the early stage of development and utilization. In recent years, BP neural network algorithm has been often used in coalbed methane prediction, but the model has some shortcomings in the training process, such as slow convergence speed, sensitive to initial value and easy to fall into local minimum value. Therefore, this paper proposed an improved BP neural network prediction model characterized by artificial bee colony algorithm. Firstly, R-type cluster analysis was used to classify the seismic attributes extracted from the 3D seismic data, four seismic attributes which are most sensitive to the change of coalbed-methane and independent of each other were selected. Secondly, the artificial bee colony algorithm(ABC) was used to find the optimal connection weight of the input layer and the hidden layer and the optimal threshold of the hidden layer of BP neural network, to build a robust ABC-BP neural network prediction model, and the seismic attributes of well location and gas content data was used as samples to train the model. Finally, the coal seam gas content in the work area was predicted by taking the optimal seismic attributes of the target reservoir in the whole work area as input. The prediction results are basically consistent with the change trend of gas content in each well. Among them, the average error rate at the training well is 0.23%, and the error rate at the verification well is less than 15%. Therefore, the prediction method has high reliability and strong applicability, and can be effectively used for coal seam gas content prediction. © 2021 Meitiandizhi Yu Kantan/Coal Geology and Exploration. All rights reserved

引用

页码：152 / 158

页数：6

共 21 条

[1] CHEN Guiwu, DONG Shouhua, WU Haibo, Et al., Research and application of quantitative geophysics recognition in high abundance of coalbed methane-rich region[J], Progress in Geophysics, 29, 5, pp. 2151-2156, (2014)
[2] SU Hai, TIAN Pengfei, DU Zhongdong, Et al., Prediction of rich accumulation area of CBM in eastern Erdos Basin: A case study of Wucheng area[J], Progress in Geophysics, 31, 2, pp. 775-781, (2016)
[3] ZHU Zhengping, LEI Kehui, PAN Renfang, Coal seam gas content prediction based on seismic multi-attributes analysis in Heshun Block, Qinshui Basin[J], Geophysical Prospecting for Petroleum, 54, 2, pp. 226-233, (2015)
[4] TANG Zhaojian, Prediction of coalbed methane enrichment area based on seismic multi attribute fusion method, (2016)
[5] QI Xuemei, Coalbed methane reservoir seismic response characteristics and applications, (2013)
[6] LI Yanfang, CHENG Jianyuan, WANG Cheng, Seismic attribute optimization based on support vector machine and coalbed methane prediction[J], Coal Geology & Exploration, 40, 6, pp. 75-78, (2012)
[7] MENG Zhaoping, TIAN Yongdong, LEI Yang, Prediction models of coal bed gas content based on BP neural networks and its applications[J], Journal of China University of Mining & Technology, 37, 4, pp. 456-461, (2008)
[8] CAO Siyuan, LIANG Chunsheng, The application of BP neural network in reservoir prediction[J], Progress in Geophysics, 17, 1, pp. 84-90, (2002)
[9] ZHANG Rui, CHEN Gang, PAN Baozhi, Et al., The prediction of the coalbed methane content based on bacteria foraging optimizing generalized regression neural network[J], Geophysical and Geochemical Exploration, 40, 2, pp. 327-332, (2016)
[10] ZHAO Tao, YU Shijian, GA-BP neural network algorithm-based nonlinear inversion for high density resistivity method[J], Coal Geology & Exploration, 45, 2, pp. 147-151, (2017)

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