烃源岩总有机碳含量测井预测模型探讨——以陆丰凹陷文昌组为例

Well logging prediction models of TOC content in source rocks:a case of Wenchang Formation in Lufeng Sag

测井参数与烃源岩总有机碳(TOC)含量之间存在某种响应关系,可以利用测井参数对TOC进行预测。建立了陆丰凹陷文昌组烃源岩TOC和电阻率曲线、声波时差曲线、中子孔隙度曲线、自然伽马曲线和密度曲线之间的多元回归模型、BP神经网络模型和曲线叠合模型,探讨了3种模型对TOC预测效果的差异。结果表明,多元回归模型对陆丰凹陷文昌组半深湖亚相、三角洲前缘亚相烃源岩的TOC预测效果较好,对滨浅湖亚相的预测效果较差;BP神经网络模型比多元回归模型预测的效果好;曲线叠合模型预测效果较差。在实际应用中,BP神经网络模型适用于测井参数与TOC难以用显式函数表达,且有足够大数据量的地层;多元回归模型适用于测井参数与TOC有明显相关性的地层;曲线叠合模型适用于伽马曲线对黏土和有机质含量响应明显的地层,并且目标曲线在非烃源岩层能较好叠合。通过对以上模型的分析,可向该坳陷其他次级凹陷推广应用。

There is a certain response relationship between well logging parameters and total organic carbon (TOC)content of source rocks,so TOC content can be predicted by well logging parameters. The multi-variate regression model,BP artificial neural network model and curve overlapping model were established between TOC and conventional well log data,including resistivity log,acoustic log,neutron porosity log,gamma-ray log and density log of Wenchang Formation source rocks in Lufeng Sag. The differences of the three models in TOC prediction effect were discussed. The results show that the multi-variate regression model has better TOC prediction effect for semi-deep lake facies and delta front facies,but worse for shore-shallow lake facies. The prediction effect of BP artificial neural network model is better than that of multi-variate regression model,while the curve overlapping model has worse prediction effect. In practical application,the BP artificial neural network model is suitable for areas where logging parameters and TOC are difficult to express with explicit functions and have a large enough data volume,the multi-variate regression model is suitable for areas where logging parameters are significantly correlated with TOC,while the curve overlapping model is suitable for areas where gamma curve responds significantly to clay and organic matter content,and the target curve can be well superposed in non-hydrocarbon source rock beds. Through the analysis of the above models,it can be applied to other sub-sags in the depression.