柴北缘牛东地区砂砾岩储层特征及分类评价  

Characteristics and classification of glutenite reservoirs in Niudong area,north margin of Qaidam Basin

作　　者：边会媛[1] 韩博华 王飞[2] 刘国良 李新虎[1] 郭正权 BIAN Hui-yuan;HAN Bo-hua;WANG Fei;LIU Guo-liang;LI Xin-hu;GUO Zheng-quan(College of Geology&Environment,Xi’an University of Science and Technology,Xi’an 710054,China;College of Geology Engineering and Geomatics,Chang’an University,Xi’an 710054,China;Changqing Branch,China Petroleum Logging Co.,Ltd.,Xi’an 710021,China;Research Institute of Exploration and Development,PetroChina Qinghai Oilfield Company,Dunhuang 736202,China)

机构地区：[1]西安科技大学地质与环境学院,陕西西安710054 [2]长安大学地质工程与测绘学院,陕西西安710054 [3]中国石油集团测井有限公司长庆分公司,陕西西安710021 [4]中国石油青海油田分公司勘探开发研究院,甘肃敦煌736202

出　　处：《西安科技大学学报》2020年第5期894-901,共8页Journal of Xi’an University of Science and Technology

基　　金：国家自然科学基金项目(41674135);陕西省自然科学基础研究计划项目(2020JQ-747);陕西省教育厅专项科学研究项目(18JK0517);中央高校基础科研业务费专项资金项目(300102260107)。

摘　　要：以柴达木盆地北缘牛东地区为代表的山前块状含砾砂岩储层岩性及孔隙结构复杂,储层非均质性强,储层参数评价困难,利用常规测井资料对储层分类效果不理想。基于孔渗、薄片分析、X衍射等岩心测试资料,明确渐新统下干柴沟组储层的岩性、岩屑、胶结物及储集空间类型等特征。核磁T2谱与毛管压力曲线均能反映储层孔隙结构特征,且核磁测井数据具有连续性,可将核磁测井数据转换为伪毛管压力曲线并对复杂砂砾岩储层进行分类。根据毛管压力曲线特征将储层分成3类,以相同岩心的压汞及核磁实验资料为基础,采用幂函数方法建立了核磁T2谱与毛管压力曲线间的转换模型,可将核磁T2谱转换为随深度连续的伪毛管压力曲线。对柴北缘牛东地区E3储层84块岩心的压汞曲线按照形态进行分类,通过广义神经网络(GRNN)实现全井段伪毛管压力曲线应用于储层类型预测,预测结果与压汞实验分类结果一致,能够为复杂砂砾岩储层类型划分提供理论指导。Conventional logging techniques fail to well classify piedmont massive pebbled sandstone reservoirs,typically in Niudong area,north margin of Qaidam Basin,due to complex lithology and pore structures,strong reservoir heterogeneity,and reservoir parameters difficult to evaluate.In such case,this paper was designed to base its exploration on such core test data as pore permeability,thin section analysis and X-ray diffraction,and then to succeed in defining the lithology,cuttings,cementation,reservoir space and reservoir type of the Lower Ganchaigou Formation of the Oligocene Epoch.Since both NMR T2 spectrum and the capillary pressure curve can reflect pore structure characteristics,NMR logging data,which are in a continuum,can be transformed into pseudo-capillary pressure curves to classify complex glutenite reservoirs.With reservoirs grouped into three types by the characteristics of their capillary pressure curves,a transformation model between the NMR T2 spectrum and the capillary pressure curve was established using power function and the experimental data of mercury intrusion and NMR of the same cores.Then the model was performed to transform the NMR T2 spectrum into a pseudo-capillary pressure curve continuing with depth.The subsequent step was to build the relationship between capillary pressure curves and reservoir types through Generalized Regression Neural Network(GRNN),and the mercury intrusion curves of 84 cores in Niudong area were classified according to their shapes.The pseudo-capillary pressure curves of the entire well section are applied in reservoir type prediction,leading to a consistent conclusion with the classification results of the above-mentioned capillary pressure curves.Hence,the current method can provide theoretical guidance for classification of glutenite reservoirs.

关 键 词：砂砾岩 核磁测井 广义神经网络 毛管压力 储层分类 

分 类 号：P631[天文地球—地质矿产勘探]