摘要
准确、全面地认识页岩孔隙结构是精准评估储量和高效开采油气的基础和关键。为此,以渤海湾盆地济阳坳陷古近系始新统沙河街组页岩为例,综合应用包括X射线计算断层扫描、大视域扫描电子显微镜、扫描电子显微镜和聚焦离子束扫描电子显微镜在内的多实验联用成像技术,获取了二维和三维多分辨率页岩微观结构图像,并结合图像处理和机器学习算法,实现了对页岩孔隙结构进行单一尺度与多尺度的同步表征。研究结果表明:①研究区页岩的孔隙空间主要由微裂缝、无机质孔隙、有机质和有机质孔隙组成,且都呈现出多尺度特征。②无机质孔隙类型多样,其中溶蚀孔隙发育丰富;有机质呈现出条状和散块状分布,部分有机质中未发现有机质孔隙。③孔隙半径小于20 nm的占比为25%,20~50 nm的占比为19%,50~100 nm的占比为29%,100~500 nm的占比为14%,500 nm~20μm的占比为11%,20~50μm的占比为2%。④有机质孔隙的连通性弱于无机孔隙,有机质孔隙与无机质孔隙的连通对油气运移起着至关重要的作用,微裂缝主导流体流动通道。⑤孔隙半径小于50 nm的孔隙基本以有机质孔隙为主,孔隙半径介于50~500 nm的孔隙为有机质孔隙和无机质孔隙,孔隙半径大于500 nm的孔隙主要贡献者为微裂缝。结论认为,单一的成像实验无法准确、全面地揭示页岩储层的多尺度微观孔隙结构,通过多种成像技术和机器学习算法的联合使用,能够同时实现页岩孔隙结构在单一尺度和多尺度下的认识和表征,提出的新方法能够准确、全面地获取页岩多尺度孔隙结构信息。
An accurate and comprehensive understanding of shale pore structure is fundamental and critical for accurate reserves evaluation and efficient hydrocarbon development.Thus,by taking the shale of Paleogene Eocene Shahejie Formation in the Jiyang Depression,Bohai Bay Basin,as an example,the 2D and 3D multi-resolution images of the shale microstructure are obtained by multiple imaging techniques,including X-ray computed tomography,large field scanning electron microscopy,scanning electron microscopy and focused ion beam scanning electron microscopy.By integrating image processing and machine learning algorithm,the shale pore structure is characterized at a single scale and multi-scale.The results are obtained as follows.First,the shale pore space in the study area is mainly composed of microfractures,inorganic pores,organic matters and organic pores,and exclusively shows multi-scale characteristics.Second,there are various types of inorganic pores,and abundant dissolution pores;organic matters are distributed as strips and patches,and no organic pores are found in some organic matters.Third,pores with radius less than 20 nm account for 25%,those with radius between 20 and 50 nm account for 19%,those with radius between 50 and 100 nm account for 29%,those with radius between 100 and 500 nm account for 14%,those with radius between 500 nm and 20μm account for 11%,and those with radius between 20 and 50μm account for 2%.Fourth,the organic pores are less connected than the inorganic pores.The connectivity between organic pores and inorganic pores plays a crucial role in hydrocarbon migration,and microfractures control fluid flow channels.Fifth,pores with radius less than 50 nm are dominantly organic pores,those with radius between 50 and 500 nm are mainly organic and inorganic pores,and microfractures mainly contribute to the pores with radius more than 500 nm.It is concluded that a single imaging experiment cannot accurately and comprehensively reveal the multi-scale microscopic pore structure of a shale reservoir.Through integration of multiple imaging techniques and machine learning algorithms,the shale pore structure can be recognized and characterized at both single scale and multi-scale.The proposed new method provides accurate and comprehensive information of multi-scale pore structures.
作者
姚军
刘磊
杨永飞
孙海
张磊
YAO Jun;LIU Lei;YANG Yongfei;SUN Hai;ZHANG Lei(School of Petroleum Engineering,China University of Petroleum<East China>,Qingdao,Shandong 266580,China;Research Centre of Multiphase Flow in Porous Media,China University of Petroleum<East China>,Qingdao,Shandong 266580,China)
出处
《天然气工业》
EI
CAS
CSCD
北大核心
2023年第1期36-46,共11页
Natural Gas Industry
基金
国家自然科学基金优秀青年科学基金项目“多尺度油气渗流力学”(编号:52122402)
国家自然科学基金重点项目“超深超高压气藏高效开采科学问题”(编号:52034010)
山东省自然科学基金杰出青年基金项目“非常规油气藏多尺度渗流理论”(编号:ZR2022JQ23)。
关键词
页岩
多尺度
多类型
孔隙结构
多实验联用成像技术
机器学习
Shale
Multi-scale
Multi-type
Pore structure
Multi-experimental imaging technology
Machine learning
