基于沉积微相特征挖掘的随机森林岩石相测井识别方法——以新场地区须家河组二段致密砂岩为例  

A log-based lithofacies identification method based on random forest andsedimentary microfacies characteristics:A case study of tight sandstonesin the second member of the Xujiahe Formation in the Xinchang area

作　　者：何小龙 张兵[1] 杨凯[1] 何一帆 李琢 HE Xiao-Long;ZHANG Bing;YANG Kai;HE Yi-Fan;LI Zhuo(Key Laboratory of Earth Exploration and Information Techniques,Ministry of Education,Chengdu University of Technology,Chengdu 610059,China)

机构地区：[1]成都理工大学地球勘探与信息技术教育部重点实验室,四川成都610059

出　　处：《物探与化探》2024年第5期1337-1347,共11页Geophysical and Geochemical Exploration

基　　金：中石化项目“川西坳陷须二段优质储层形成机理与‘甜点’预测”(AH2022-0577)。

摘　　要：致密砂岩是天然气和石油的重要储层之一,通过致密砂岩岩石相的识别,可以更加深入地了解储层发育特征。采用岩心观察和测井数据处理相结合,分析新场地区致密砂岩岩石相与沉积微相的特征以及内部联系,通过沉积微相特征数据挖掘,构建具有地质内涵的随机森林分类模型。结果表明:(1)致密砂岩可划分为泥岩、沙纹层理粉砂岩、块状细砂岩、平行层理细砂岩、块状中粗砂岩、平行层理中粗砂岩、交错层理中粗砂岩7种典型岩石相;(2)研究区主要沉积微相为水下分流河道、水下分流间湾、河口坝以及前三角洲泥,且与岩石相的沉积联系紧密;(3)分类模型中可以将沉积微相内GR曲线的相对重心(RM)、变差方根差(GS)、平均中位数(AM)以及平均斜率(M)作为特征参数,增加数据集的特征数;(4)考虑沉积微相特征尤其是水体能量与水体动荡情况,可以显著提升随机森林分类模型性能。研究结果为机器学习方法识别岩石相提供了新思路,并为致密砂岩油气勘探提供了重要的参考。Tight sandstones serve as significant oil and gas reservoirs.Their lithofacies identification can assist in further understanding the developmental characteristics of reservoirs.Combining core observations with log data processing,this study analyzed the lithofacies and sedimentary microfacies characteristics of tight sandstones in the Xinchang area and the internal relationships between lithofacies and sedimentary microfacies.Moreover,it constructed a random forest classification model with geological implications through data mining of sedimentary microfacies characteristics.The results show that:(1)Tight sandstones in the Xinchang area can be classified into seven typical lithofacies,including mudstone,siltstone with ripple lamination,massive fine sandstone,fine sandstone with parallel bedding,massive medium-to coarse-grained sandstone,and medium-to coarse-grained sandstone with parallel/cross bedding;(2)The sedimentary microfacies in the Xinchang area consist primarily of subaqueous distributary channel,subaqueous distributary bay,river-mouth bar,and prodeltaic mud,which are closely associated with the sedimentation of lithofacies;(3)In the classification model,the relative centroid(RM),root mean square deviation(GS),average median(AM),and average slope(M)of the gamma ray(GR)curve can be used as the characteristic parameters of sedimentary microfacies to increase the number of characteristics in the dataset;(4)Considering the characteristics of sedimentary microfacies,especially the energy and turbulence of water bodies,can significantly enhance the performance of the random forest classification model.Overall,the results of this study provide a novel approach for lithofacies identification using machine learning methods and a significant reference for oil and gas exploration in tight sandstones.

关 键 词：沉积微相 随机森林 岩石相 须家河组 致密砂岩 

分 类 号：P631.4[天文地球—地质矿产勘探]