岩心智能识别技术内涵与展望

doi:10.7623/syxb202408011

石油学报 ›› 2024, Vol. 45 ›› Issue (8): 1296-1308.DOI: 10.7623/syxb202408011

• 综述 • 上一篇

岩心智能识别技术内涵与展望

刘合^1,2, 任义丽^1,2,3, 李欣^1,2,3, 朱如凯², 胡延旭⁴, 刘茜^2,3, 苏乾潇^2,3, 吴健平⁴, 李彬⁴

1. 多资源协同陆相页岩油绿色开采全国重点实验室黑龙江大庆 163712;
2. 中国石油勘探开发研究院北京 100083;
3. 中国石油天然气集团有限公司勘探开发人工智能技术研发中心北京 100083;
4. 中国石油华北油田公司勘探开发研究院河北任丘 062552

收稿日期:2024-01-03 修回日期:2024-04-24 发布日期:2024-09-04
通讯作者: 任义丽，女，1987年3月生，2023年获中国石油勘探开发研究院博士学位，现为中国石油勘探开发研究院高级工程师，主要从事计算机视觉、深度学习、大模型等技术在油气地质中的应用研究。Email:renyili@petrochina.com.cn
作者简介:刘合，男，1961年3月生，2002年获哈尔滨工程大学博士学位，现为中国工程院院士、中国石油勘探开发研究院副总工程师，主要从事低渗透油气藏增产改造、机采系统提高系统效率、分层注水和井筒工程控制技术、油气人工智能等研究。Email:liuhe@petrchina.com.cn
基金资助:
国家自然科学基金面上项目“知识与数据融合的油气储层薄片智能鉴定方法”(No.42372175)和中国石油天然气股份有限公司科技项目“油气勘探开发人工智能关键技术研究”(2023DJ84)资助。

Connotation and prospect of intelligent recognition technology for cores

Liu He^1,2, Ren Yili^1,2,3, Li Xin^1,2,3, Zhu Rukai², Hu Yanxu⁴, Liu Xi^2,3, Su Qianxiao^2,3, Wu Jianping⁴, Li Bin⁴

1. National Key Laboratory for Green Mining of Multi-resource Collabrative Continental Shale Oil, Heilongjiang Daqing 163712, China;
2. PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China;
3. CNPC Artificial Intelligence Technology R&D Center for Exploration and Development, Beijing 100083, China;
4. Exploration and Development Research Institute, PetroChina Huabei Oilfield Company, Hebei Renqiu 062550, China

Received:2024-01-03 Revised:2024-04-24 Published:2024-09-04

摘要/Abstract

摘要： 岩心分析可为油气成烃成储成藏史研究、提高采收率和寻找优质储量提供支撑。随着油气勘探开发转向深层和非常规领域,储层非均质性强,原有基于岩心的单点式分析已不能满足需要,须将多种尺度的岩心图像和岩心实验数据进行综合分析。岩心分析从传统的人工描述,发展到现在的数字岩心并向岩心智能识别的方向发展。通过概括岩心图像分析的国内外研究现状,提出了岩心智能识别技术的定义和内涵;以利用微米—纳米CT图像重构全直径岩心孔隙结构的高分辨率CT图像为例,对岩心智能识别技术进行了阐述;对岩心智能识别技术在储层评价、压裂方案设计、微观渗流机理研究等领域的应用进行了展望。岩心智能识别技术的提出反映了人工智能技术在油气领域已经开始同步升级发展,即从单点业务智能化、提速提效的初级阶段,向着多尺度多模态数据融合、垂直领域大模型技术应用、提质发展的更高阶段转变。

关键词: 人工智能, 岩心智能识别, 岩石组分, 孔隙结构, 岩石结构

Abstract: Core analysis can provide support for studying the history of hydrocarbon generation, reservoir formation, and petroleum accumulation, improving oil and gas recovery rates, and searching for large-scale high-quality reserves. With the hydrocarbon exploration and development shifting towards deep and unconventional fields, the reservoirs are highly heterogeneous, and so the previous single-point analysis based on core can no longer meet the needs. It is necessary to comprehensively analyze the multi-scale images and experimental data of cores. Moreover, core analysis has developed from conventional manual description to the current digital core technology, and further towards the intelligent recognition of cores. Firstly, the paper comprehensively summarizes the current research status of core image analysis at home and abroad, and then proposes the definition and connotation of intelligent recognition technology for cores; next, the intelligent recognition of cores has been elaborated based on the case study of how to reconstruct the high-resolution CT images of full-diameter pore structure using micro-nano CT images; finally, the application of intelligent recognition technology for cores in reservoir evaluation, fracturing scheme design, and micro-seepage mechanism research is prospected. The proposal of intelligent recognition technology for cores reflects that artificial intelligence technology has begun to upgrade and develop synchronously in the oil and gas field, i.e., from the primary stage of intelligentization and speed and efficiency improvement of single-point business to a higher stage of multi-scale and multi-modal data fusion, application of large model technology in vertical fields, as well as high-quality development.

Key words: artificial intelligence, intelligent recognition of cores, rock constituents, pore structure, rock structure

中图分类号:

TE122.14

刘合, 任义丽, 李欣, 朱如凯, 胡延旭, 刘茜, 苏乾潇, 吴健平, 李彬. 岩心智能识别技术内涵与展望[J]. 石油学报, 2024, 45(8): 1296-1308.

Liu He, Ren Yili, Li Xin, Zhu Rukai, Hu Yanxu, Liu Xi, Su Qianxiao, Wu Jianping, Li Bin. Connotation and prospect of intelligent recognition technology for cores[J]. Acta Petrolei Sinica, 2024, 45(8): 1296-1308.

参考文献

[1] 王宗礼, 娄钰, 潘继平.中国油气资源勘探开发现状与发展前景[J].国际石油经济, 2017, 25(3):1-6.
WANG Zongli, LOU Yu, PAN Jiping.China’s oil & gas resources exploration and development and its prospect[J].International Petroleum Economics, 2017, 25(3):1-6.
[2] 白林, 姚钰, 李双涛, 等.基于深度学习特征提取的岩石图像矿物成分分析[J].中国矿业, 2018, 27(7):178-182.
BAI Lin, YAO Yu, LI Shuangtao, et al.Mineral composition analysis of rock image based on deep learning feature extraction[J].China Mining Magazine, 2018, 27(7):178-182.
[3] 冯雅兴, 龚希, 徐永洋, 等.基于岩石新鲜面图像与孪生卷积神经网络的岩性识别方法研究[J].地理与地理信息科学, 2019, 35(5): 89-94.
FENG Yaxing, GONG Xi, XU Yongyang, et al.Lithology recognition based on fresh rock images and twins convolution neural network[J]. Geography and Geo-Information Science, 2019, 35(5):89-94.
[4] FAN Guangpeng, CHEN Feixiang, CHEN Danyu, et al.Recognizing multiple types of rocks quickly and accurately based on lightweight CNNs model[J].IEEE Access, 2020, 8:55269-55278.
[5] 熊越晗, 刘东燕, 刘东升, 等.基于岩样细观图像深度学习的岩性自动分类方法[J].吉林大学学报(地球科学版), 2021, 51(5):1597-1604.
XIONG Yuehan, LIU Dongyan, LIU Dongsheng, et al.Automatic lithology classification method based on deep learning of rock sample meso-image[J].Journal of Jilin University (Earth Science Edition), 2021, 51(5):1597-1604.
[6] LI Na, HAO Huizhen, GU Qing, et al.A transfer learning method for automatic identification of sandstone microscopic images[J].Computers & Geosciences, 2017, 103:111-121.
[7] 雷明锋, 张运波, 王卫东, 等.岩石岩性Mask R-CNN智能识别方法与应用研究[J].铁道科学与工程学报, 2022, 19(11):3372-3382.
LEI Mingfeng, ZHANG Yunbo, WANG Weidong, et al.Investigation and application on lithology intelligent recognition method based on mask R-CNN[J].Journal of Railway Science and Engineering, 2022, 19(11):3372-3382.
[8] 彭伟航, 白林, 商世为, 等.基于改进InceptionV3模型的常见矿物智能识别[J].地质通报, 2019, 38(12):2059-2066.
PENG Weihang, BAI Lin, SHANG Shiwei, et al.Common mineral intelligent recognition based on improved InceptionV3[J].Geological Bulletin of China, 2019, 38(12):2059-2066.
[9] 张中亚.砂岩薄片图像分割与识别研究[D].合肥:中国科学技术大学, 2020.
ZHANG Zhongya.Research on image segmentation and recognition of sandstone thin section[D].Hefei:University of Science and Technology of China, 2020.
[10] 郭艳军, 周哲, 林贺洵, 等.基于深度学习的智能矿物识别方法研究[J].地学前缘, 2020, 27(5):39-47.
GUO Yanjun, ZHOU Zhe, LIN Hexun, et al.The mineral intelligence identification method based on deep learning algorithms[J].Earth Science Frontiers, 2020, 27(5):39-47.
[11] BORGES H P, DE AGUIAR M S.Mineral classification using machine learning and images of microscopic rock thin section[C]//18th Mexican International Conference on Artificial Intelligence:Advances in Soft Computing.Xalapa:Springer, 2019:63-76.
[12] MAITRE J, BOUCHARD K, BÉDARD L P.Mineral grains recognition using computer vision and machine learning[J].Computers & Geosciences, 2019, 130:84-93.
[13] 黄辉红.基于深度学习的泥岩岩性与风化程度检测[D].成都:电子科技大学, 2022.
HUANG Huihong.Detection of mudstone lithology and weathering degree based on deep learning[D].Chengdu:University of Electronic Science and Technology of China, 2022.
[14] 严良平, 彭泽豹, 葛家晟, 等.基于深度学习的岩石风化度分类方法:114048803A[P].2022-02-15.
YAN Liangping, PENG Zebao, GE Jiasheng, et al.Classification method of rock weathering degree based on deep learning:114048803A[P].2022-02-15.
[15] CHEN Zhuoheng, LIU Xiaojun, YANG Jijin, et al.Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in western Canada Sedimentary Basin[J].Computers & Geosciences, 2020, 138:104450.
[16] WANG Yingda, SHABANINEJAD M, ARMSTRONG R T, et al.Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images[J].Applied Soft Computing, 2021, 104:107185.
[17] LI Chunxiao, WANG Dongmei, KONG Lingyun.Application of machine learning techniques in mineral classification for scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS) images[J].Journal of Petroleum Science and Engineering, 2021, 200:108178.
[18] LI Bingke, NIE Xin, CAI Jianchao, et al.U-Net model for multi-component digital rock modeling of shales based on CT and QEMSCAN images[J].Journal of Petroleum Science and Engineering, 2022, 216:110734.
[19] 司晨冉, 王仁超, 邸阔, 等.一种基于Mask R-CNN和分水岭算法的岩石颗粒图像分割方法[J].水电能源科学, 2020, 38(11):129-132.
SI Chenran, WANG Renchao, DI Kuo, et al.A rock particle image segmentation method based on mask R-CNN and watershed algorithm[J]. Water Resources and Power, 2020, 38(11):129-132.
[20] 王伟, 李擎, 张德政, 等.基于深度学习的矿石图像处理研究综述[J].工程科学学报, 2023, 45(4):621-631.
WANG Wei, LI Qing, ZHANG Dezheng, et al.A survey of ore image processing based on deep learning[J].Chinese Journal of Engineering, 2023, 45(4):621-631.
[21] 王浩, 熊淑华, 何海波, 等.基于改进UNet3+的岩心图像颗粒提取算法[J].计算机系统应用, 2024, 33(1):199-205.
WANG Hao, XIONG Shuhua, HE Haibo, et al.Core image particle extraction algorithm based on improved UNet3+[J].Computer Systems & Applications, 2024, 33(1):199-205.
[22] 薛章涛, 张航, 潘少伟, 等.基于改进U-Net的岩心铸体薄片图像分割研究[J].甘肃科学学报, 2023, 35(4):9-14.
XUE Zhangtao, ZHANG Hang, PAN Shaowei, et al.Study on image segmentation of core founding slice based on improved U-Net[J]. Journal of Gansu Sciences, 2023, 35(4):9-14.
[23] TANG Kunning, WANG Yingda, MOSTAGHIMI P, et al.Deep convolutional neural network for 3D mineral identification and liberation analysis[J].Minerals Engineering, 2022, 183:107592.
[24] 陈雁, 李祉呈, 程超, 等.FLU-net:用于表征页岩储层微观孔隙的深度全卷积网络[J].海洋地质前沿, 2021, 37(8):34-43.
CHEN Yan, LI Zhicheng, CHENG Chao, et al.FLU-net:a deep fully convolutional neural network for shale reservoir micro-pore characterization [J].Marine Geology Frontiers, 2021, 37(8):34-43.
[25] JOBE T D, VITAL-BRAZIL E, KHAIT M.Geological feature prediction using image-based machine learning[J].Petrophysics, 2018, 59(6):750-760.
[26] DUARTE-CORONADO D, TELLEZ-RODRIGUEZ J, PIRES DE LIMA R, et al.Deep convolutional neural networks as an estimator of porosity in thin-section images for unconventional reservoirs[C]//SEG Technical Program Expanded Abstracts 2019.Texas:Society of Exploration Geophysicists, 2019:3181-3184.
[27] MISBAHUDDIN M.Estimating petrophysical properties of shale rock using conventional neural networks CNN[R].SPE 204272, 2020.
[28] FLORES A G R, FISHER Q, LORINCZI P.Convolutional neural networks for the classification of the microstructure of tight sandstone[C]//International Petroleum Technology Conference.Texas:IPTC, 2021:IPTC-21208-MS.
[29] ALQAHTANI N, ARMSTRONG R T, MOSTAGHIMI P.Deep learning convolutional neural networks to predict porous media properties[R].SPE 191906, 2018.
[30] ANTLE R.Automated core fracture characterization by computer vision and image analytics of CT images[R].SPE 195181, 2019.
[31] KIRILLOV A, MINTUN E, RAVI N, et al.Segment anything[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV).Paris:IEEE, 2023:3992-4003.
[32] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al.Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems.Montreal:NIPS, 2014:2672-2680.
[33] MOSSER L, DUBRULE O, BLUNT M J.Reconstruction of three-dimensional porous media using generative adversarial neural networks[J]. Physical Review E, 2017, 96(4):043309.
[34] MOSSER L, DUBRULE O, BLUNT M J.Stochastic reconstruction of an oolitic limestone by generative adversarial networks[J].Transport in Porous Media, 2018, 125(1):81-103.
[35] LIU X F, PENG J H, ZHANG J X, et al.Research on characterization methods of efflorescence on cement-based decorative mortar[J].IOP Conference Series:Materials Science and Engineering, 2019, 504:012007.
[36] 杨永飞, 刘夫贵, 姚军, 等.基于生成对抗网络的页岩三维数字岩芯构建[J].西南石油大学学报(自然科学版), 2021, 43(5):73-83.
YANG Yongfei, LIU Fugui, YAO Jun, et al.Reconstruction of 3D shale digital rock based on generative adversarial network[J].Journal of Southwest Petroleum University (Science & Technology Edition), 2021, 43(5):73-83.
[37] ZHAO Jiuyu, WANG Fuyong, CAI Jianchao.3D tight sandstone digital rock reconstruction with deep learning[J].Journal of Petroleum Science and Engineering, 2021, 207:109020.
[38] FENG Junxi, TENG Qizhi, LI Bing, et al.An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning[J].Computer Methods in Applied Mechanics and Engineering, 2020, 368:113043.
[39] VOLKHONSKIY D, MURAVLEVA E, SUDAKOV O, et al.Reconstruction of 3D porous media from 2D slices[R/OL].(2021-08-06).https://arxiv.org/pdf/1901.10233.
[40] ZHENG Qiang, ZHANG Dongxiao.RockGPT:reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning[J].Computational Geosciences, 2022, 26(3):677-696.
[41] DONG Chao, LOY C C, HE Kaiming, et al.Image super-resolution using deep convolutional networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2):295-307.
[42] WANG Yingda, ARMSTRONG R T, MOSTAGHIMI P.Enhancing resolution of digital rock images with super resolution convolutional neural networks[J].Journal of Petroleum Science and Engineering, 2019, 182:106261.
[43] LEDIG C, THEIS L, HUSZÁR F, et al.Photo-realistic single image super-resolution using a generative adversarial network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu:IEEE, 2017:105-114.
[44] 朱联祥, 郑逸.自注意力SRGAN在岩石CT图像超分辨中的应用研究[J].西安石油大学学报(自然科学版), 2022, 37(2):131-137.
ZHU Lianxiang, ZHENG Yi.Applications of self-attention SRGAN in super resolution reconstruction of rock CT image[J].Journal of Xi’an Shiyou University (Natural Science Edition), 2022, 37(2):131-137.
[45] CHEN Honggang, HE Xiaohai, TENG Qizhi, et al.Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks[J].Physical Review E, 2020, 101(2):023305.
[46] 姜黎明, 刘宁静, 孙建孟, 等.利用CT图像与压汞核磁共振构建高精度三维数字岩心[J].测井技术, 2016, 40(4):404-407.
JIANG Liming, LIU Ningjing, SUN Jianmeng, et al.Higher precision 3D digital core constructed by CT scanning image with mercury penetration and NMR[J].Well Logging Technology, 2016, 40(4):404-407.
[47] WANG Yu, PU Jie, WANG Lihua, et al.Characterization of typical 3D pore networks of Jiulaodong Formation shale using Nano-transmission X-ray microscopy[J].Fuel, 2016, 170:84-91.
[48] 崔利凯, 孙建孟, 闫伟超, 等.基于多分辨率图像融合的多尺度多组分数字岩心构建[J].吉林大学学报(地球科学版), 2017, 47(6): 1904-1912.
CUI Likai, SUN Jianmeng, YAN Weichao, et al.Construction of multi-scale and -component digital cores based on fusion of different resolution core images[J].Journal of Jilin University (Earth Science Edition), 2017, 47(6):1904-1912.
[49] 李俊键, 成宝洋, 刘仁静, 等.基于数字岩心的孔隙尺度砂砾岩水敏微观机理[J].石油学报, 2019, 40(5):594-603.
LI Junjian, CHENG Baoyang, LIU Renjing, et al.Microscopic mechanism of water sensitivity of pore-scale sandy conglomerate based on digital core[J].Acta Petrolei Sinica, 2019, 40(5):594-603.
[50] 陶金雨, 张昌民, 郭旭光, 等.磨圆度定量表征在扇三角洲沉积微相判别中的应用——以玛湖凹陷百口泉组砾岩为例[J].沉积学报, 2020, 38(5):956-965.
TAO Jinyu, ZHANG Changmin, GUO Xuguang, et al.Application of quantitative roundness characterization to identify sedimentary microfacies in fan delta deposits:a case study of conglomerates in the Baikouquan Formation, Mahu sag[J].Acta Sedimentologica Sinica, 2020, 38(5):956-965.
[51] 陶金雨, 张昌民, 朱锐.基于岩芯图像的砾石磨圆度测量方法:105953766A[P].2016-09-21.
TAO Jinyu, ZHANG Changmin, ZHU Rui.Gravel grinding roundness measurement method based on core images:105953766A[P].2016-09-21.
[52] 双棋.砾石磨圆度定量方法探究[J].资源信息与工程, 2019, 34(1) :103-105.
SHUANG Qi.Exploration on the quantitative method of gravel grinding roundness[J].Resource Information and Engineering, 2019, 34(1):103-105.
[53] 任义丽.基于深度学习的油气储层薄片智能鉴定方法[D].北京:中国石油勘探开发研究院, 2023.
REN Yili.Rock thin-section of reservoir analysis and identification based on artificial intelligent technique[D].Beijing:PetroChina Research Institute of Petroleum Exploration & Development, 2023.
[54] WU Yuqi, TAHMASEBI P, LIN Chengyan, et al.A comprehensive investigation of the effects of organic-matter pores on shale properties:a multicomponent and multiscale modeling[J].Journal of Natural Gas Science and Engineering, 2020, 81:103425.
[55] 吴玉其.低渗透储层数字岩心分析及微观剩余油研究[D].青岛:中国石油大学(华东), 2021.
WU Yuqi.Digital core analysis of low-permeability reservoirs and research on microscopic remaining oil[D].Qingdao:China University of Petroleum (East China), 2021.

岩心智能识别技术内涵与展望

Connotation and prospect of intelligent recognition technology for cores

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	计秉玉, 张文彪, 何应付, 段太忠, 刘合. 油藏地质建模与数值模拟一体化内涵及发展趋势[J]. 石油学报, 2024, 45(7): 1152-1162.
[2]	惠沙沙, 庞雄奇, 谌卓恒, 王琛茜, 施砍园, 胡涛, 胡耀, 李敏, 梅术星, 黎茂稳. 四川盆地海相页岩孔隙类型对孔隙空间贡献定量表征[J]. 石油学报, 2024, 45(3): 531-547.
[3]	刘合, 李艳春, 贾德利, 王素玲, 乔美霞, 屈如意, 温鹏云, 任智慧. 人工智能在注水开发方案精细化调整中的应用现状及展望[J]. 石油学报, 2023, 44(9): 1574-1586.
[4]	陈义林, 秦勇, 张博, 杨天宇, 刘静, 王兰花, 洪勇. 制样粒径对不同变质程度煤孔隙结构的差异化影响[J]. 石油学报, 2023, 44(8): 1313-1332.
[5]	丁骞, 甘利灯, 魏乐乐, 张宇生, 杨昊, 姜晓宇, 王峣钧. 双孔隙结构因子及其在储层渗透性地震预测中的应用[J]. 石油学报, 2023, 44(2): 339-347.
[6]	刘喜武, 王心宇, 刘宇巍, 张金强, 刘炯, 刘倩. 中国陆相页岩油地震勘探技术现状及发展方向[J]. 石油学报, 2023, 44(12): 2270-2285.
[7]	唐淑玲, 汤达祯, 杨焦生, 邓泽, 李松, 陈世达, 冯鹏, 黄晨, 李站伟. 鄂尔多斯盆地大宁—吉县区块深部煤储层孔隙结构特征及储气潜力[J]. 石油学报, 2023, 44(11): 1854-1866,1902.
[8]	方正, 蒲秀刚, 陈世悦, 鄢继华, 陈星燃, 崔绮梦. 陆相湖盆深水区页岩高频层序对储层发育的影响——以渤海湾盆地沧东凹陷孔店组二段页岩为例[J]. 石油学报, 2023, 44(10): 1663-1682.
[9]	金国文, 王堂宇, 刘忠华, 谢然红, 邵亮, 李博宇. 基于核磁共振测井的砂砾岩储层分类与产能预测方法[J]. 石油学报, 2022, 43(5): 648-657.
[10]	刘文岭, 韩大匡. 数字孪生油气藏:智慧油气田建设的新方向[J]. 石油学报, 2022, 43(10): 1450-1461.
[11]	孙金声, 刘凡, 程荣超, 冯杰, 郝惠军, 王韧, 白英睿, 刘钦政. 机器学习在防漏堵漏中研究进展与展望[J]. 石油学报, 2022, 43(1): 91-100.
[12]	黄兴, 窦亮彬, 左雄娣, 高辉, 李天太. 致密油藏裂缝动态渗吸排驱规律[J]. 石油学报, 2021, 42(7): 924-935.
[13]	李宁, 徐彬森, 武宏亮, 冯周, 李雨生, 王克文, 刘鹏. 人工智能在测井地层评价中的应用现状及前景[J]. 石油学报, 2021, 42(4): 508-522.
[14]	李童, 龙安林, 刘波, 丁晓军, 于慧敏, 鲁珊珊, 宋彦辰, 石开波. 低渗透砂岩油藏隔夹层注气突破压力及注气开发策略——以柴达木盆地尕斯库勒油田E₃¹油藏为例[J]. 石油学报, 2021, 42(10): 1364-1372.
[15]	黄兴, 倪军, 李响, 薛俊杰, 柏明星, 周彤. 致密油藏不同微观孔隙结构储层CO₂驱动用特征及影响因素[J]. 石油学报, 2020, 41(7): 853-864.