摘要
页岩油由于储层异常致密,传统储层表征方法存在取心成本高、岩心测试周期长等问题,并且实验室测量方法对页岩储集空间、微观孔隙结构的精细刻画不足。为此提出了一种基于岩屑定量数字化分析的页岩油储层表征方法,通过对岩屑的扫描电子显微镜(SEM)图片进行基于深度学习的图像识别和分割,刻画出纳米级孔隙结构,再结合J函数分析方法,定量分析页岩油储层最低可动孔隙半径,用以辅助页岩油储层的高效开发。研究选取吉木萨尔芦草沟页岩油储层典型井吉174井岩石碎屑样品为研究对象,得到了该井页岩油储层可动用油的孔隙尺寸下限,同时核磁测井结果也验证了该方法的有效性。
For the reservoirs bearing shale oil,due to their abnormal tightness,there are many problems,such as high coring cost and long core testing period,in characterizing these reservoirs with traditional methods.Furthermore,it is also insufficient for laboratory measurement methods to finely characterize the shale reservoir space and microscopic pore structure.To this end,a method characterizing shale oil reservoirs based on quantitative digital analysis of cuttings is proposed.Based on deep learning,the scanning electron microscope(SEM)images of cuttings were recognized and segmented to characterize the nano-scale pore structure,and then combined with the J-function analysis method,the minimum movable pore radius of shale oil reservoirs was quantitatively analyzed to assist the efficient development of shale oil reservoirs.The study selects the cutting sample from Well Ji 174,a typical well in the Lucaogou shale oil reservoir in Jimsar,as the research object,and gets the lower limit of the pore size that can allow oil movement,in addition,the nuclear magnetic logging results also verified the effectiveness of the method.
作者
姚树新
程浩然
熊钊
王冠群
徐东升
龙威
YAO Shuxin;CHENG Haoran;XIONG Zhao;WANG Guanqun;XU Dongsheng;LONG Wei(CNOOC Information Technology Co.,Ltd.,Shenzhen 518068,Guangdong,China;Research Institute of Tsinghua University in Shenzhen,Shenzhen 518057,Guangdong,China;Icore Group INC,Shenzhen 518057,Guangdong,China;Research Institute of Exploration and Development,Xinjiang Oilfield Company,Karamay 834000,Xinjiang,China)
出处
《石油钻采工艺》
CAS
北大核心
2022年第1期117-122,共6页
Oil Drilling & Production Technology
基金
深圳市海外高层次人才创新创业专项资金“服务南海油气精准探采的岩心微流动数字化技术平台”(编号:KQTD2017033114582189)
广东省引进创新创业团队项目“服务南海油气精准探采的微流动数字化智能决策平台研发与应用”(编号:2017ZT07G264)。
关键词
岩屑定量数字化分析
储层表征
数字岩心
SEM数字化分析
核磁测井
J函数
quantitative digital cuttings analysis
reservoir characterization
digital core
SEM digital analysis
nuclear magnetic logging
J function