Total organic carbon is a very important parameter in logging evaluation for source rocks and is also important to determine the hydrocarbon potential of source rocks and the prediction of adsorption gas content.To analyze the prediction method of TOC, taking the source rocks of block H in Canada as example, the correlation relationship between TOC and the logging is analyzed on the basis of the conventional logging, the laboratory analysis TOC are collected.Based on the results of correlation analysis results, GR, RHOB, NPHI, PEFZ, URAN and ratio of deep resistivity to shallow resistivity are selected as the basic parameters, and BP neural network prediction model for TOC content is established.The TOC content predicted by the BP neural network model is also compared with the results from laboratory.The application of BP neural network prediction model of TOC in block H of Canada shows that the neural network prediction model of TOC based on conventional logs has very high accuracy.It also shows a good application prospect.It is of great significance for analyzing the hydrocarbon potential and the gas content prediction of source rocks in block H.%总有机碳是烃源岩测井评价中一个非常重要的参数.对于确定烃源岩生烃潜力分析以及吸附气含量预测有重要意义.为了研究总有机碳含量的预测方法,以加拿大H区块烃源岩为例,在收集测井资料、实验分析总有机碳含量基础上,分析总有机碳含量与常规测井资料之间的相关性,以相关性分析结果为依据,选择自然伽玛、补偿密度、中子孔隙度、光电吸收截面、自然伽玛能谱-铀以及深浅电阻率的相对变化作为基础参数,建立总机碳含量的BP神经网络预测模型,对预测的总有机碳含量与实验室分析结果加以对比与分析.应用结果表明:基于测井资料相关性分析建立的总有机碳含量BP神经网络预测模型对于总有机碳含量预测具有较高精度,表现出很好的应用前景.该模型对H区块后续烃源岩的生烃潜力有利区分析和吸附气含气量预测具有积极意义.
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