【摘要】 总有机碳含量(total organic carbon, TOC)是评价页岩气藏生烃能力的重要指标，对页岩气藏地质“甜点”的准确预测至关重要。现有页岩气藏TOC含量预测方法存在主观性强、泛化能力弱等缺点，以川南海相页岩气藏为研究对象，通过对研究区块测井资料和实验室岩心分析结果的整理，优选出自然伽马、密度等测井参数作为模型训练的特征向量，建立总有机碳含量的多层前馈神经网络(back propagation, BP)和支持向量机预测模型，分析不同模型之间的差异，对模型特征组合、网络结构等影响因素进行分析，最后将预测的TOC结果与真实值对比。结果表明：基于不含能谱测井资料的BP神经网络预测模型更能真实地反映出测井资料与储层的非线性关系，为TOC的预测提供新的思路。更多还原

【Abstract】 The total organic carbon(TOC) is an important index for evaluating the hydrocarbon generation capacity of shale gas reservoirs. It is crucial for accurate prediction of the geological “sweet spot” of shale gas reservoirs. The existing methods for predicting the TOC content of shale gas reservoirs are highly subjective and have weak generalization ability. The offshore shale gas reservoir in southern Sichuan was taken as the research object. By sorting out the logging data of the study block and the results of laboratory core analysis, logging parameters such as natural gamma and density were selected as the feature vectors for model training. BP neural network and support vector machine prediction models of total organic carbon content were established to analyze the differences between different models. The influence factors such as model feature combination and network result were analyzed. Finally, the predicted TOC results were compared with the real values. The results show that the prediction model based on BP neural network without energy spectrum logging data can reflect the nonlinear relationship between logging data and reservoir more truly, and could provide a new idea for TOC prediction.更多还原