Interpreting coal component content in logging data by combining grey relational analysis and hybrid neural network

IF 1.1 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Interpretation-A Journal of Subsurface Characterization Pub Date : 2023-07-19 DOI:10.1190/int-2022-0077.1
Ze Bai, Qinjie Liu, M. Tan, Yang Bai, Haibo Wu
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Abstract

The coal component content is an important parameter during the coal resources exploration and exploitation. Previous logging curve regression and single neural network methods have the disadvantages of low accuracy and weak generalization ability in calculating coal component content. In this study, a GRA-HNN method was proposed by combining grey relational analysis (GRA) and hybrid neural network (HNN) to predict coal component content in logging data. First, the correlation degree between different conventional logging data and coal components was calculated using the GRA method, and logging curves with a correlation degree = 0.7 were selected as the input training data set. Then, a back propagation neural network (BPNN), support vector machine (SVM) neural network, and radial basis function (RBF) neural network of different coal components were constructed based on the selected optimal input logging data, and the weighted average strategy was used to form a HNN prediction model. Finally, the GRA-HNN method was used to predict the coal component content of coalbed methane production wells in Panji mining area. The application results showed that the coal component content predicted by the GRA-HNN method has the highest accuracy compared to the logging curve regression method and its single neural network model, with a maximum average relative error of 13.4%. Besides, the accuracy of coal component content predicted by some single intelligent models is not always higher than the logging curve regression method, indicating that the neural network model is not necessarily suitable for all coal component content predictions. The proposed GRA-HNN method not only optimizes the prediction performance of a single neural network model by selecting effective input parameters, but also comprehensively considers the prediction effect of several neural network models, which strengthens the generalization ability of neural network model and increases the log interpretation accuracy of coal component content.
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结合灰色关联分析和混合神经网络对测井资料中煤组分含量进行解释
煤组分含量是煤炭资源勘探开发过程中的一个重要参数。以往的测井曲线回归和单神经网络方法在计算煤组分含量方面存在精度低、泛化能力弱的缺点。本研究将灰色关联分析(GRA)和混合神经网络(HNN)相结合,提出了一种预测测井数据中煤组分含量的GRA-HNN方法。首先,使用GRA方法计算不同常规测井数据与煤组分之间的相关性,并选择相关性为0.7的测井曲线作为输入训练数据集。然后,基于所选择的最优输入测井数据,构建了不同煤组分的反向传播神经网络(BPNN)、支持向量机(SVM)神经网络和径向基函数(RBF)神经网络,并采用加权平均策略形成HNN预测模型。最后,利用GRA-HNN方法对潘集矿区煤层气生产井的煤组分含量进行了预测。应用结果表明,与测井曲线回归方法及其单神经网络模型相比,GRA-HNN方法预测的煤组分含量具有最高的精度,最大平均相对误差为13.4%,一些单一智能模型预测煤组分含量的准确性并不总是高于测井曲线回归方法,这表明神经网络模型不一定适用于所有煤组分的预测。所提出的GRA-HNN方法不仅通过选择有效的输入参数优化了单个神经网络模型的预测性能,而且综合考虑了多个神经网络模型预测效果,增强了神经网络模型泛化能力,提高了煤组分含量的测井解释精度。
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来源期刊
CiteScore
2.50
自引率
8.30%
发文量
126
期刊介绍: ***Jointly published by the American Association of Petroleum Geologists (AAPG) and the Society of Exploration Geophysicists (SEG)*** Interpretation is a new, peer-reviewed journal for advancing the practice of subsurface interpretation.
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