[1] 孙建孟,孙晓娟,迟蓬,等.数字岩心和数字井筒技术研究与应用进展[J].石油物探,2023,62(5):806-819.
SUN Jianmeng,SUN Xiaojuan,CHI Peng,et al.Digital cores and digital wellbore technology:application and research progress[J].Geophysical Prospecting for Petroleum,2023,62(5):806-819.
[2] 门相勇,王陆新,王越,等.新时代我国油气勘探开发战略格局与2035年展望[J].中国石油勘探,2021,26(3):1-8.
MEN Xiangyong,WANG Luxin,WANG Yue,et al.Strategic pattern of China′s oil and gas exploration and development in the new era and prospects for 2035[J].China Petroleum Exploration,2021,26(3):1-8.
[3] XUE Liang,LI Daolun,DOU Hongen.Artificial intelligence methods for oil and gas reservoir development:current progresses and perspectives[J].Advances in Geo-Energy Research,2023,10(1):65-70.
[4] 贾承造,郑民,张永峰.中国非常规油气资源与勘探开发前景[J].石油勘探与开发,2012,39(2):129-136.
JIA Chengzao,ZHENG Min,ZHANG Yongfeng.Unconventional hydrocarbon resources in China and the prospect of exploration and development[J].Petroleum Exploration and Development,2012,39(2):129-136.
[5] 张国印,林承焰,王志章,等.知识与数据融合驱动的油气藏智能表征及研究进展[J].地球物理学进展,2024,39(1):119-140.
ZHANG Guoyin,LIN Chengyan,WANG Zhizhang,et al.Hybrid knowledge-driven and data-driven intelligent reservoir characterization and its research progress[J].Progress in Geophysics,2024,39(1):119-140.
[6] 张聪,韩慧萍,王艳红,等.基于数据共享的油气钻井岩心数字分析系统[J].东北石油大学学报,2022,46(6):88-99.
ZHANG Cong,HAN Huiping,WANG Yanhong,et al.A data-sharing-based digital analysis system for oil and gas drilling cores[J].Journal of Northeast Petroleum University,2022,46(6):88-99.
[7] 姚军,赵秀才,衣艳静,等.数字岩心技术现状及展望[J].油气地质与采收率,2005,12(6):52-54.
YAO Jun,ZHAO Xiucai,YI Yanjing,et al.The current situation and prospect on digital core technology[J].Petroleum Geology and Recovery Efficiency,2005,12(6):52-54.
[8] 赵建鹏,陈惠,李宁,等.三维数字岩心技术岩石物理应用研究进展[J].地球物理学进展,2020,35(3):1099-1108.
ZHAO Jianpeng,CHEN Hui,LI Ning,et al.Research advance of petrophysical application based on digital core technology[J].Progress in Geophysics,2020,35(3):1099-1108.
[9] ALZUBAIDI F,MOSTAGHIMI P,SWIETOJANSKI P,et al.Automated lithology classification from drill core images using convolutional neural networks[J].Journal of Petroleum Science and Engineering,2021,197:107933.
[10] 景文龙,李博涵,杨守磊,等.基于TensorFlow的均质数字岩心渗透率预测方法及应用[J].中国石油大学学报(自然科学版),2021,45(4):108-113.
JING Wenlong,LI Bohan,YANG Shoulei,et al.Method and application of homogeneous digital core permeability prediction based on TensorFlow[J].Journal of China University of Petroleum(Edition of Natural Sciences),2021,45(4):108-113.
[11] 赵秀才.数字岩心及孔隙网络模型重构方法研究[D].东营:中国石油大学(华东),2010.
ZHAO Xiucai.Research on digital core and pore network model reconstruction method[D].Dongying:China University of Petroleum(East China),2010.
[12] HAZLETT R D.Statistical characterization and stochastic modeling of pore networks in relation to fluid flow[J].Mathematical Geology,1997,29(6):801-822.
[13] BAKKE S,REN P E.3-D pore-scale modelling of sandstones and flow simulations in the pore networks[J].SPE Journal,1997,2(2):136-149.
[14] DVORKIN J,WALLS J,TUTUNCU A,et al.Third international meeting for applied geoscience & energy expanded abstracts[C].Tulsa:Society of Exploration Geophysicists,2003:1521-1524.
[15] 匡立春,刘合,任义丽,等.人工智能在石油勘探开发领域的应用现状与发展趋势[J].石油勘探与开发,2021,48(1):1-11.
KUANG Lichun,LIU He,REN Yili,et al.Application and development trend of artificial intelligence in petroleum exploration and development[J].Petroleum Exploration and Development,2021,48(1):1-11.
[16] 王晓琦,金旭,李建明,等.聚焦离子束扫描电镜在石油地质研究中的综合应用[J].电子显微学报,2019,38(3):303-319.
WANG Xiaoqi,JIN Xu,LI Jianming,et al.FIB-SEM applications in petroleum geology research[J].Journal of Chinese Electron Microscopy Society,2019,38(3):303-319.
[17] 吴翔,肖占山,张永浩,等.多尺度数字岩石建模进展与展望[J].吉林大学学报(地球科学版),2024,54(5):1736-1751.
WU Xiang,XIAO Zhanshan,ZHANG Yonghao,et al.Progress and prospect of multiscale digital rock modeling[J].Journal of Jilin University(Earth Science Edition),2024,54(5):1736-1751.
[18] 朱伟.基于过程法的数字岩心建模方法研究[J].地球物理学进展,2020,35(4):1539-1544.
ZHU Wei.Study on digital rock reconstruction using the process-based method[J].Progress in Geophysics,2020,35(4):1539-1544.
[19] 宋帅兵,张通.各向异性多孔介质数字岩心模型重构通用算法[J].地球物理学报,2023,66(11):4765-4780.
SONG Shuaibing,ZHANG Tong.General algorithm for reconstructing digital core model of anisotropic porous media[J].Chinese Journal of Geophysics,2023,66(11):4765-4780.
[20] LI Xiaobin,LI Bingke,LIU Fangzhou,et al.Advances in the application of deep learning methods to digital rock technology[J].Advances in Geo-Energy Research,2023,8(1):5-18.
[21] GOODFELLOW J I,POUGET-ABADIE J,MIRZA M,et al.NIPS′14:neural information processing systems,December 8-13,2014[C].Cambridge:MIT Press,2014:2672-2680.
[22] LUKAS Mosser,OLIVIER Dubrule,J MARTIN J Blunt.Reconstruction of three-dimensional porous media using generative adversarial neural networks[J].Physical Review E,2017,96(4):043309.
[23] 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.
[24] YOU N,LI Y E,CHENG A.First international meeting for applied geoscience & energy expanded abstracts[C].Tulsa:Society of Exploration Geophysicists,2021:1815-1819.
[25] 李秉科,聂昕,朱林奇,等.基于梯度惩罚-生成对抗神经网络的页岩三维数字岩心重构[J].西安石油大学学报(自然科学版),2023,38(2):53-60.
LI Bingke,NIE Xin,ZHU Linqi,et al.Reconstruction of 3D shale digital core based on generative adversarial neural networks with gradient penalty[J].Journal of Xi′an Shiyou University(Natural Science Edition),2023,38(2):53-60.
[26] FENG Junxi,HE Xiaohai,TENG Qizhi,et al.Reconstruction of porous media from extremely limited information using conditional generative adversarial networks[J].Physical Review E,2019,100(3):033308.
[27] 段太忠,张文彪,何治亮,等.塔里木盆地顺北油田超深断溶体深度学习地质建模方法[J].石油与天然气地质,2023,44(1):203-212.
DUAN Taizhong,ZHANG Wenbiao,HE Zhiliang,et al.Deep learning-based geological modeling of ultra-deep fault-karst reservoirs in Shunbei Oilfield,Tarim Basin[J].Oil & Gas Geology,2023,44(1):203-212.
[28] JOHAN P,LEONARDO R,GABRIEL K,et al.Size-invariant 3D generation from a single 2D rock image[J].Journal of Petroleum Science and Engineering,2022,215(PB):110684.
[29] ZHANG Ting,XIA Pengfei,LU Fangfang.3D reconstruction of digital cores based on a model using generative adversarial networks and variational auto-encoders[J].Journal of Petroleum Science and Engineering,2021,207(4):109151.
[30] WANG Hongsheng,DALTON Laura,GUO Ruichang,et al.Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO2-water-rock system[J].Advances in Water Resources,2023,173:104385.
[31] KANG Jia,LI Nianyin,ZHAO Liqiang,et al.Construction of complex digital rock physics based on full convolution network[J].Petroleum Science,2022,19(2):651-662.
[32] 赵岩龙,张永安,李星宇,等.基于改进BP神经网络的岩心图像分割方法研究[J].西安石油大学学报(自然科学版),2020,35(4):114-120.
ZHAO Yanlong,ZHANG Yong′an,LI Xingyu,et al.A core image segmentation method based on improved BP neural network[J].Journal of Xi′an Shiyou University(Natural Science Edition),2020,35(4):114-120.
[33] 欧发辉,赖富强,夏小雪,等.一种页岩气储层有机孔提取及测井计算新模型[J].断块油气田,2023,30(5):705-714.
OU Fahui,LAI Fuqiang,XIA Xiaoxue,et al.A new model of organic pore extraction and logging calculation for shale gas reservoir[J].Fault-Block Oil & Gas Field,2023,30(5):705-714.
[34] RONNEBERGER O,FISCHER P,BROX T.Medical image computing and computer-assisted intervention-MICCAI 2015[C].Cham:Springer,2015:234-241.
[35] 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,2022,261:110734.
[36] WU Xiang,WANG Fei,ZHANG Xiaoqiu,et al.Extraction of fractures in shale CT images using improved U-Net[J].Energy Geoscience.2024,5(2):100185.
[37] RUSHOOD I A,ALQAHTANI N,WANG Y D,et al.Segmentation of X-ray images of rocks using deep learning[C].SPE201282-MS,2020:1-13.
[38] WANG Hongsheng,DALTON Laura,FAN Ming,et al.Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM[J].Journal of Petroleum Science and Engineering,2022,215(PA):110596.
[39] KARIMPOULI S,TAHMASEBI P.Segmentation of digital rock images using deep convolutional autoencoder networks[J].Computers and Geosciences,2019,126(5):142-150.
[40] 杜李慧,邹长春,彭诚.基于Mask R-CNN的岩心图像砾石颗粒智能分割方法初探:以松科二井为例[C]//徐义刚.2021年中国地球科学联合学术年会论文集.北京:北京伯通电子出版社,2021:121-122.
DU Lihui,ZOU Changchun,PENG Cheng.Preliminary study on intelligent segmentation method of gravel particles in core images based on Mask R-CNN:a case study of Well Songke Ⅱ[C]//XU Yigang.Proceeding of the 2021 annual meeting of Chinese geoscience union.Beijing:Beijing Borton Electronic Publishing House,2021:121-122.
[41] KIRILLOV A,MINTUN E,RAVI N,et al.Segment anything: a promptable model for image segmentation[C] //Program Committee.Proceedings of the IEEE/CVF international conference on computer vision.New York:IEEE,2023:4015-4026.
[42] 刘博伟,李彦来,杨磊,等.基于格子玻尔兹曼方法的数字岩心孔隙流动模拟[J].新疆石油天然气,2020,16(1):45-50.
LIU Bowei,LI Yanlai,YANG Lei,et al.Mulation of digital core pore flow based on lattice Boltzmann method[J].Xinjiang Oil & Gas,2020,16(1):45-50.
[43] 印兴耀,郑颖,宗兆云,等.数字岩心逆建模理论下的储层参数定量预测方法[J].地球物理学报,2019,62(2):720-729.
YIN Xingyao,ZHENG Ying,ZONG Zhaoyun,et al.Estimation of reservoir properties with inverse digital rock physics modeling approach[J].Chinese Journal of Geophysics,2019,62(2):720-729.
[44] 杨笑,王志章,周子勇,等.基于参数优化AdaBoost算法的酸性火山岩岩性分类[J].石油学报,2019,40(4):457-467.
YANG Xiao,WANG Zhizhang,ZHOU Ziyong,et al.Lithology classification of acidic volcanic rocks based on parameter-optimized AdaBoost algorithm[J].Acta Petrolei Sinica,2019,40(4):457-467.
[45] WANG Kun,CHEN Yu,MEHANA Mohamed,et al.A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media[J].Journal of Computational Physics,2021,443:110526.
[46] 房娜,刘宗宾,岳宝林,等.裂缝性油藏渗流特征及注水策略[J].特种油气藏,2024,31(3):91-97.
FANG Na,LIU Zongbin,YUE Baolin,et al.Seepage characteristics and water injection strategy of fractured reservoir[J].Special Oil & Gas Reservoirs,2024,31(3):91-97.
[47] ALI K,TAPAN M.Prediction of fluid flow in porous media by sparse observations and physics-informed pointnet[J].Neural Networks,2023,167:80-91.
[48] YAN Bicheng,HARP Dylan Robert,CHEN Bailian,et al.A physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media[J].Fuel,2022,313:122693.
[49] KASHEFI A,MUKERJI T.Prediction of fluid flow in porous media by sparse observations and physics-informed pointnet[J].Journal of Petroleum Science and Engineering,2022,208:109205.
[50] XIE Chiyu,ZHU Jingwei,WANG Jiulong,et al.Direct prediction of relative permeability curve from 3D digital rock images based on deep learning approaches[J].International Journal of Rock Mechanics and Mining Sciences,2023,170:105544.
[51] 刘晶晶.基于人工智能的致密砂岩优质储层预测[D].西安:长安大学,2023.
LIU Jingjing.Prediction of high-quality tight sandstone reservoirs based on artificial intelligence[D].Xi′an:Yangtze University,2023.
[52] TEMBELY M,ALSUMAITI A M,ALAMERI W S.Machine and deep learning for estimating the permeability of complex carbonate rock from X-ray micro-computed tomography[J].Energy Reports,2021,7:1460-1472.
[53] YANG Yongfei,LIU Fugui,YAO Jun,et al.Multi-scale reconstruction of porous media from low-resolution core images using conditional generative adversarial networks[J].Journal of Natural Gas Science and Engineering,2022,99:104411.
[54] 王敏.基于砂砾岩多组分三维数字岩心的电阻率数值模拟与影响规律分析[J].油气地质与采收率,2024,31(6):33-44.
WANG Min.Resistivity numerical simulation and its influence law analysis based on multi-component 3D digital core of glutenite[J].Petroleum Geology and Recovery Efficiency,2024,31(6):33-44.
[55] 杨光,高祥成,李永忠,等.岩心数字化技术研究与应用[J].油气地质与采收率,2024,31(4):217-226.
YANG Guang,GAO Xiangcheng,LI Yongzhong,et al.Research and application of digital core technologies[J].Petroleum Geology and Recovery Efficiency,2024,31(4):217-226.
[56] 杨雪,廖锐全,袁旭. 基于核磁共振技术的致密岩心高温高压自发渗吸实验[J].大庆石油地质与开发,2023,42(3):58-65.
YANG Xue,LIAO Ruiquan,YUAN Xu. Spontaneous imbibition experiment in high-temperature and high-pressure for tight cores based on NMR technology[J].Petroleum Geology & Oilfield Development in Daqing,2023,42(3):58-65.
[57] 郭建华,佘朝毅,夏连彬,等.基于数字岩心模型重构的岩石力学行为评价新方法[J].天然气工业,2024,44(7):132-143.
GUO Jianhua, SHE Chaoyi, XIA Lianbin, et al. A new rock mechanical behavior evaluation method reconstructed based on the digital core model[J]. Natural Gas Industry, 2024, 44(7): 132-143.
[58] 李阳,廉培庆,薛兆杰,等.大数据及人工智能在油气田开发中的应用现状及展望[J].中国石油大学学报(自然科学版),2020,44(4):1-11.
LI Yang,LIAN Peiqing,XUE Zhaojie,et al.Application status and prospect of big data and artificial intelligence in oil and gas field development[J].Journal of China University of Petroleum(Edition of Natural Sciences),2020,44(4):1-11.
[59] XIE Chiyu,DU Shuyi,WANG Jiulong,et al.Intelligent modeling with physics-informed machine learning for petroleum engineering problems[J].Advances in Geo-Energy Research,2023,8(2):71-75.
[60] CHU Hongyang,DONG Peng,LEE W John.A deep-learning approach for reservoir evaluation for shale gas wells with complex fracture networks[J].Advances in Geo-Energy Research,2023,7(1):49-65.
辽公网安备 21110002000031号 辽ICP备18011648号-1
