陈雁 , 焦世祥 , 程超 , 黄成 , 蒋裕强 . 基于自编码器的半监督隔夹层识别方法[J]. 特种油气藏, 2021 , 28(1) : 86 -91 . DOI: 10.3969/j.issn.1006-6535.2021.01.012

[1] 张斌,朱光有,苏劲,等.塔北隆起西部东河塘油气来源及分布[J].地学前缘,2012,19(4):276-283.
ZHANG Bin,ZHU Guangyou,SU Jin,et al.Origin and distribution of hydrocarbon in Donghetang Oifield,west Tabei Uplift,Tarim Basin[J].Earth Science Frontiers,2012,19(4):276-283.
[2] 李维禄,徐怀民,王超,等.基于动、静模式约束的隔、夹层空间分布综合预测——以塔里木盆地东河塘油田井区为例[J].西北大学学报(自然科学版),2016,46(6):877-886.
LI Weilu,XU Huaimin,WANG Chao,et al.Spatial distribution prediction of interlayers based on dynamic evaluation:a case of Donghetang sandstone in Donghetang Oil Field,Tarim Basin[J].Journal of Northwest University(Natural Science Edition),2016,46(6):877-886.
[3] 邓亚,郭睿,田中元,等.碳酸盐岩储集层隔夹层地质特征及成因——以伊拉克西古尔纳油田白垩系Mishrif组为例[J].石油勘探与开发,2016,43(1):136-144.
DENG Ya,GUO Rui,TIAN Zhongyuan,et al.Geologic features and genesis of the barriers and intercalations in carbonates:a case study of the Cretaceous Mishrif Formation,West Qurna Oil Field,Iraq[J].Petroleum Exploration and Development,2016,43(1):136-144.
[4] 印森林,吴胜和,冯文杰,等.冲积扇储集层内部隔夹层样式——以克拉玛依油田一中区克下组为例[J].石油勘探与开发,2013,40(6):757-763.
YIN Senlin,WU Shenghe,FENG Wenjie,et al.Patterns of inter-layers in the alluvial fan reservoirs:a case study on Triassic Lower Karamay Formation,Yizhong Area,Karamay Oilfield,NW China[J].Petroleum Exploration and Development,2013,40(6):757-763.
[5] 杜全伟,王维斌,刘志军,等.辽河西部凹陷大凌河油层隔夹层特征[J].特种油气藏,2015,22(3):74-76,154.
DU Quanwei,WANG Weibin,LIU Zhijun,et al.Characteristics of the intercalations in Dalinghe reservoir of the western sag,Liaohe Basin[J].Special Oil & Gas Reservoirs,2015,22(3):74-76,154.
[6] 马成龙.特厚层稠油油藏隔夹层识别研究[J].特种油气藏,2018,25(2):111-115.
MA Chenglong.Interlayer identification in ultra-thick heavy oil reservoir[J].Special Oil & Gas Reservoirs,2018,25(2):111-115.
[7] 孙天建,穆龙新,赵国良.砂质辫状河储集层隔夹层类型及其表征方法——以苏丹穆格莱特盆地Hegli油田为例[J].石油勘探与开发,2014,41(1):112-120.
SUN Tianjian,MU Longxin,ZHAO Guoliang.Classification and characterization of barrier-intercalation in sandy braided river reservoirs:taking Hegli Oilfield of Muglad Basin in Sudan as an example[J].Petroleum Exploration and Development,2014,41(1):112-120.
[8] 周学慧,丁文龙,昌伦杰,等.“三端员定型”法识别滨岸相砂岩储层隔夹层:以塔里木盆地哈得逊油田东河砂岩为例[J].地学前缘,2017,24(5):328-338.
ZHOU Xuehui,DING Wenlong,CHANG Lunjie,et al.Three-end-number diagram of classification to identify the interlayers of sandstone reservoir in littoral facies: a case from Carboniferous Donghe sandstone in Hade Oil Field,Tarim Basin,NW China[J].Earth Science Frontiers,2017,24(5):328-338.
[9] 周国文,谭成仟,郑小武,等.H油田隔夹层测井识别方法研究[J].石油物探,2006,45(5):542-545.
ZHOU Guowen,TAN Chengqian,ZHENG Xiaowu,et al.Research on recognition ofbarrier/interbed via logging in H Oilfield[J].Geophysical Prospecting for Petroleum,2006,45(5):542-545.
[10] 张吉,张烈辉,胡书勇,等.陆相碎屑岩储层隔夹层成因、特征及其识别[J].测井技术,2003,22(4):221-224.
ZHANG Ji,ZHANG Liehui,HU Shuyong,et al.The genesis and characteristics and identification of intercalations in non-marine reservoir with clastic rock[J].Well Logging Technology,2003,22(4):221-224.
[11] 谷建伟,周梅,李志涛,等.基于数据挖掘的长短期记忆网络模型油井产量预测方法[J].特种油气藏,2019,26(2):77-81,131.
GU Jianwei,ZHOU Mei,LI Zhitao,et al.Oil well production forecast with long-short term memory network model based on data mining[J].Special Oil & Gas Reservoirs,2019,26(2):77-81,131.
[12] 李克文,周广悦,路慎强,等.一种基于机器学习的有利区评价新方法[J].特种油气藏,2019,26(3):7-11.
LI Kewen,ZHOU Guangyue,LU Shenqiang,et al.A new method for favorable zone evaluation based on machine learning[J].Special Oil & Gas Reservoirs,2019,26(3):7-11.
[13] 张东晓,陈云天,孟晋.基于循环神经网络的测井曲线生成方法[J].石油勘探与开发,2018,45(4):598-607.
ZHANG Dongxiao,CHEN Yuntian,MENG Jin.Synthetic well logs generation via recurrent neural networks[J].Petroleum Exploration and Development,2018,45(4):598-607.
[14] 瞿子易,罗鑫,谢润成,等.小波神经网络在隔夹层研究中的应用[J].石油天然气学报,2009,31(4):265-268.
QU Ziyi,LUO Xin,XIE Runcheng,et al.Application of WNN in the studies of interbeds[J].Journal of Oil and Gas Technology,2009,31(4):265-268.
[15] SHAHSHAHANI B M,LANDGREBE D A.The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J].IEEE Transactions of Geoscience & Remote Sensing,1994,32(5):1087-1095.
[16] YANG Zhimin ,CAO Xinxia,SHAO Yuanhai.Weak fuzzy support vector machine based on credibility measure[J].Journal of Convergence Information Technology,2012,7(10):322-329.
[17] VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11(12):3371-3408.
[18] KIM K H,BAE J W.Amplification methods bias metagenomic libraries of uncultured single-stranded and double-stranded DNA viruses[J].Applied and Environmental Microbiology,2011,77(21):7663-7668.
[19] FAN Z,BI D,HE L,et al.Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder[J].Neurocomputing,2017,243:12-20.
[20] SHIN M,MUN S,HAN D K,et al.New generalized sidelobe canceller with denoising auto-encoder for improved speech enhancement[J].Ice Transactions on Fundamentals of Electronics Communications & Computerences,2017,100(12):3038-3040.
[21] WEN L,GAO L,LI X.A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2019,49(1):136-144.
[22] 孙文珺,邵思羽,严如强.基于稀疏自动编码深度神经网络的感应电动机故障诊断[J].机械工程学报,2016,52(9):65-71.
SUN Wenjun,SHAO Siyu,YAN Ruqiang.Induction motor fault diagnosis based on deep neural network of sparse auto-encoder[J].Journal of Mechanical Engineering,2016,52(9):65-71.
[23] DU B,XIONG W,WU J,et al.Stacked convolutional denoising auto-encoders for feature representation[J].IEEE Transactions on Cybernetics,2017,47(4):1017-1027.