基于优选地震属性与PSO-BP模型的煤层含气量预测

doi:10.11720/wtyht.2020.1551

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Abstract

Conventional coal seam gas content prediction methods are mostly based on logging constrained seismic attribute inversion and linear mapping model,which makes the prediction accuracy difficult to control,and severely limits the universality of the method.This paper starts with two aspects:seismic attribute optimization and BP neural network prediction model improvement.Using the Q-type clustering analysis method,the seismic attributes of the extracted target reservoirs are classified and optimized,and four kinds of seismic attributes with good correlation with the geological targets are obtained.The particle swarm optimization algorithm is further used to BP neural network algorithm.The connection weights of the input layer and the hidden layer and the threshold of the hidden layer are optimized,and the PSO-BP prediction model is constructed.The PSO-BP model is trained by using the preferred seismic attributes and gas content data of the well location.Based on the trained PSO-BP model,the coal seam gas content prediction in the study area is carried out with the preferred seismic attributes of the entire work area as input.The comparison between the predicted gas content of the well position and the measured results shows that the prediction method has high accuracy.Therefore,it can be considered that the PSO-BP prediction model and the corresponding prediction method flow can be effectively applied to the prediction of coal gas content in coal reservoirs.

Key words： coal seam gas content prediction particle swarm optimization neural network seismic attribute optimization

Received: 25 November 2019 Published: 29 December 2020

P631.4

Corresponding Authors: WU Hai-Bo E-mail: wuhaibocumt@163.com

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	Articles by authors
	Zi-Jing ZANG
	Hai-Bo WU
	Hai DING
	Ping-Song ZHANG
	Shou-Hua DONG

Cite this article:

Zi-Jing ZANG,Hai-Bo WU,Hai DING, et al. Prediction of coalbed methane content based on preferred seismic attributes and PSO-BP model[J]. Geophysical and Geochemical Exploration, 2020, 44(6): 1381-1386.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2020.1551 OR https://www.wutanyuhuatan.com/EN/Y2020/V44/I6/1381

Overview of study area

Typical seismic profile after process

Correlation coefficient between each property of well location and gas content of coal seam

Seismic attribute clustering analysis

Preferred seismic attribute map
a—dip;b—thin bed;c—instanteous amplitude;d—instanteous Q

BP neural network structure

Particle swarm improvement BP neural network flow chart

Forecast model flow chart

Target coal reservoir gas content prediction results map

Comparison of predicted and measured values of well locations

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