基于PCA-CNN模型的页岩储层有机碳含量预测方法

Prediction method of total organic carbon in shale oil reservoir based on PCA-CNN model

总有机碳含量(TOC)是评价烃源岩有机质丰度和生烃潜力的指标之一。以东营凹陷牛庄洼陷页岩油取心井为例,以实验分析岩心TOC和测录井资料为基础,应用交会图获取TOC相关敏感参数。通过采用常规陆相页岩储层TOC计算模型即ΔlogR法和多元回归分析法预测研究区湖相页岩储层TOC,相关性不高,效果不佳。因此提出选用机器学习模型即利用主成分分析(PCA)模型与改进的卷积神经(CNN)模型组合,形成PCA-CNN模型,通过PCA模型对数据降维,去除冗余信息和噪声信息,再利用CNN模型进行页岩储层TOC预测,使样本数据质量和TOC预测精度得以提高。将PCA-CNN模型应用到牛庄洼陷的6口页岩油取心井进行TOC预测,结果表明,对于陆相页岩储层,PCA-CNN模型TOC预测精度较高,符合率最高达96%。

Total organic carbon(TOC)is one of the indicators for evaluating the organic matter abundance and hydrocarbon generation potential of hydrocarbon source rock.In this paper,taking the cored well of shale oil reservoir in Niuzhuang De⁃pression,Dongying Sag as an example,the TOC-related sensitive parameters were obtained through cross plots based on the experimental analysis of TOC from core and logging data.The conventional TOC calculation models for continental shale oil reservoir,namely theΔlogR method and the multiple regression analysis method,were used to predict the TOC of well and lake facies shale oil reservoir in the study area,but the correlation and performance were not good.Therefore,this paper proposed combining machine learning models,i.e.,the principal component analysis(PCA)model and an improved convolutional neural network(CNN)model,to form the PCA-CNN model.In this model,the PCA model was employed to re⁃duce the dimensions of data and remove redundant information and noise information,and then,the CNN model was used to predict the TOC of shale oil reservoir,which could improve sample data quality and prediction accuracy of TOC.The PCA CNN model was applied to predict the TOC of six cored wells shale oil reservoir in Niuzhuang Depression,and the results reveal that for continental shale oil reservoir,the proposed model can accurately predict TOC,and the compliance rate is up to 96%.