烃源岩有机碳测井预测模型优选及应用——以鄂尔多斯盆地安塞地区延长组长9为例

Optimization and Application of Organic Carbon Logging Prediction Models for Source Rocks:A Case Study of Chang 9 Member of Yanchang Formation in Ansai Area,Ordos Basin

总有机碳(TOC)质量分数是烃源岩评价的重要指标。为了对鄂尔多斯盆地东南部安塞地区延长组长9烃源岩有机碳进行测井评价,本文先立足于岩心分析实测w(TOC)资料,基于烃源岩对不同测井曲线的响应特征,运用多元回归模型、传统Δlog R模型以及Δlog R模型的改进型和广义型,分别建立烃源岩w(TOC)测井定量预测模型;然后将这几种模型加以分析和组合运用,从改进Δlog R模型中提取拟合叠合系数应用到两种广义Δlog R模型的计算当中,应用效果良好;最后对模型进行对比和优选,提出最适合研究区的烃源岩w(TOC)测井定量预测模型。结果表明:考虑密度的广义Δlog R模型准确度最高,平均相对误差为7.78%;多元回归模型次之,平均相对误差为9.65%。二者均满足w(TOC)测井定量预测的精度要求。

Total organic carbon(TOC)mass fraction is an important index of source rocks evaluation.In order to evaluate the organic carbon of source rocks in Chang 9 Member of Yanchang Formation in Ansai area,southeast Ordos basin,firstly,this article establishes w(TOC)models for quantitative prediction of well logging by applying the multiple regression model,the traditionalΔlog R model,the improvedΔlog R model and the generalizedΔlog R model,based on core analysis of measured w(TOC)data and the response characteristics of source rocks to different logging curves.Secondly,by analyzing and combining these models,the fitting superposition coefficient extracted from the improvedΔlog R model is applied to the calculation of two generalizedΔlog R models,and the application effect is good.Finally,the four models are compared and optimized,and the most suitable quantitative prediction model for source rocks in the study area is proposed.The results show that the generalizedΔlog R model considering the density factor has the highest accuracy,with an average relative error of 7.78%;The multiple regression model has the second highest accuracy,with an average relative error of 9.65%.Both of them can meet the accuracy requirements of quantitative prediction of w(TOC).