[1] |
李宁, 徐彬森, 武宏亮, 等. 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.
doi: 10.7623/syxb202104008
|
|
LI Ning, XU Binsen, WU Hongliang, et al. Application status and prospects of artificial intelligence in well logging and formation evaluation[J]. Acta Petrolei Sinica, 2021, 42(4):508-522.
doi: 10.7623/syxb202104008
|
[2] |
申辉林, 方鹏, 刘美杰. 用测井资料自动识别低孔低渗储集层岩性:以泌阳凹陷安棚古近系为例[J]. 新疆石油地质, 2010, 31(6):644-646.
|
|
SHEN Huilin, FANG Peng, LIU Meijie. Using well log data to automatically identify lithology of reservoir with low porosity and low permeability:An example from Anpeng Paleogene in Biyang sag[J]. Xinjiang Petroleum Geology, 2010, 31(6):644-646.
|
[3] |
WANG Guochang, JU Yiwen, HUANG Cheng, et al. Longmaxi-Wufeng shale lithofacies identification and 3-D modeling in the northern Fuling gas field,Sichuan basin[J]. Journal of Natural Gas Science and Engineering, 2017, 47:59-72.
|
[4] |
FORD D, KELLY M, ANONYMOUS. Using neural networks to predict lithology from well logs[J]. SEG Annual Meeting Expanded Technical Program Abstracts with Biographies, 2001, 71:666-667.
|
[5] |
刘跃杰, 刘书强, 马强, 等. BP神经网络法在三塘湖盆地芦草沟组页岩岩相识别中的应用[J]. 岩性油气藏, 2019, 31(4):101-111.
|
|
LIU Yuejie, LIU Shuqiang, MA Qiang, et al. Application of BP neutral network method to identification of shale lithofacies of Lucaogou formation in Santanghu basin[J]. Lithologic Reservoirs, 2019, 31(4):101-111.
|
[6] |
AL-ANAZI A, GATES I D. On the capability of support vector machines to classify lithology from well logs[J]. Natural Resources Research, 2010, 19(2):125-139.
|
[7] |
周新锐, 王喜鑫, 李少华, 等. 陆相混积型页岩储集层孔隙结构特征及其控制因素[J]. 新疆石油地质, 2023, 44(4):411-420.
|
|
ZHOU Xinrui, WANG Xixin, LI Shaohua, et al. Pore structure characteristics and controlling factors of continental mixed shale reservoirs[J]. Xinjiang Petroleum Geology, 2023, 44(4):411-420.
|
[8] |
BHATTACHARYA S, MISHRA S. Applications of machine learning for facies and fracture prediction using Bayesian network theory and random forest:Case studies from the Appalachian basin,USA[J]. Journal of Petroleum Science & Engineering, 2018, 170:1005-1017.
|
[9] |
钟庆良, 唐海, 石秀平, 等. 潜江凹陷潜江组盐间页岩油岩石物理建模研究[J]. 石油物探, 2020, 59(4):505-516.
doi: 10.3969/j.issn.1000-1441.2020.04.002
|
|
ZHONG Qingliang, TANG Hai, SHI Xiuping, et al. Rock physics modeling of inter-salt shale oil in the Qianjiang formation of Qianjiang sag,China[J]. Geophysical Prospecting for Petroleum, 2020, 59(4):505-516.
doi: 10.3969/j.issn.1000-1441.2020.04.002
|
[10] |
孙予舒, 黄芸, 梁婷, 等. 基于XGBoost算法的复杂碳酸盐岩岩性测井识别[J]. 岩性油气藏, 2020, 32(4):98-106.
|
|
SUN Yushu, HUANG Yun, LIANG Ting, et al. Identification of complex carbonate lithology by logging based on XGBoost algorithm[J]. Lithologic Reservoirs, 2020, 32(4):98-106.
|
[11] |
王胜, 赖昆, 张拯, 等. 基于随钻振动信号与深度学习的岩性智能预测方法[J]. 煤田地质与勘探, 2023, 51(9):51-63.
|
|
WANG Sheng, LAI Kun, ZHANG Zheng, et al. Intelligent lithology prediction method based on vibration signal while drilling and deep learning[J]. Coal Geology & Exploration, 2023, 51(9):51-63.
|
[12] |
安鹏, 曹丹平. 基于深度学习的测井岩性识别方法研究与应用[J]. 地球物理学进展, 2018, 33(3):1029-1034.
|
|
AN Peng, CAO Danping. Research and application of logging lithology identification based on deep learning[J]. Progress in Geo physics, 2018, 33(3):1029-1034.
|
[13] |
武中原, 张欣, 张春雷, 等. 基于LSTM循环神经网络的岩性识别方法[J]. 岩性油气藏, 2021, 33(3):120-128.
|
|
WU Zhongyuan, ZHANG Xin, ZHANG Chunlei, et al. Lithology identification based on LSTM recurrent neural network[J]. Lithologic Reservoirs, 2021, 33(3):120-128.
|
[14] |
牟丹, 王祝文, 黄玉龙, 等. 基于SVM测井数据的火山岩岩性识别:以辽河盆地东部坳陷为例[J]. 地球物理学报, 2015, 58(5):1785-1793.
doi: 10.6038/cjg20150528
|
|
MOU Dan, WANG Zhuwen, HUANG Yulong, et al. Lithological identification of volcanic rocks from SVM well logging data:Case study in the eastern depression of Liaohe basin[J]. Chinese Journal of Geophysics, 2015, 58(5):1785-1793.
|
[15] |
李洪奇, 谭锋奇, 许长福, 等. 基于决策树方法的砾岩油藏岩性识别[J]. 测井技术, 2010, 34(1):16-21.
|
|
LI Hongqi, TAN Fengqi, XU Changfu, et al. Lithology identification of conglomerate reservoir based on decision tree method[J]. Well Logging Technology, 2010, 34(1):16-21.
|
[16] |
谷宇峰, 张道勇, 鲍志东. PSO-GBDT识别致密砂岩储集层岩性研究:以姬塬油田西部长4+5段为例[J]. 矿物岩石地球化学通报, 2021, 40(3):624-634.
|
|
GU Yufeng, ZHANG Daoyong, BAO Zhidong, et al. Lithology prediction of tight sandstone reservoirs using the PSO-GBDT:A case study of the Chang 4+5 members in the western Jiyuan oilfield[J]. Bulletin of Mineralogy,Petrology and Geochemistry, 2021, 40(3):624-634.
|
[17] |
潘拓, 马鑫, 谢安, 等. 利用主成分分析法优化BP神经网络模型在砂砾岩岩性识别中的应用[J]. 新疆地质, 2020, 38(3):417-420.
|
|
PAN Tuo, MA Xin, XIE An, et al. Application of the optimized BP neural network model based on principal component analysis in lithology identification of glutenite reservoirs[J]. Xinjiang Geology, 2020, 38(3):417-420.
|
[18] |
王志宏, 韩璐, 戚磊. 随机森林分类方法在储层岩性识别中的应用[J]. 辽宁工程技术大学学报(自然科学版), 2015, 34(9):1083-1088.
|
|
WANG Zhihong, HAN Lu, QI Lei. Random forest classification method in the application of reservoir lithology recognition[J]. Journal of Liaoning Technical University(Natural Science), 2015, 34(9):1083-1088.
|
[19] |
崔俊峰, 杨金路, 王民, 等. 基于随机森林算法的泥页岩孔隙度预测[J]. 油气地质与采收率, 2023, 30(6):13-21.
|
|
CUI Junfeng, YANG Jinlu, WANG Min, et al. Shale porosity prediction based on random forest algorithm[J]. Petroleum Geology and Recovery Efficiency, 2023, 30(6):13-21.
|
[20] |
柴明锐, 程丹, 张昌民, 等. 机器学习方法对砂砾岩岩屑成分的预测:以西北缘X723井百口泉组为例[J]. 西安石油大学学报(自然科学版), 2017, 32(5):22-28.
|
|
CHAI Mingrui, CHENG Dan, ZHANG Changmin, et al. Prediction of debris composition in glutenite by machine learning method:A case study in Baikouquan formation of Well X723 in the NW margin of Junggar basin[J]. Journal of Xi’an Shiyou University (Natural Science Edition), 2017, 32(5):22-28.
|
[21] |
吴辰文, 梁靖涵, 王伟, 等. 基于递归特征消除方法的随机森林算法[J]. 统计与决策, 2017(21):60-63.
|
|
WU Chenwen, LIANG Jinghan, WANG Wei, et al. Random forest algorithm based on recursive feature elimination[J]. Statistics & Decision, 2017(21):60-63.
|
[22] |
周雪晴, 张占松, 张超谟, 等. 基于粗糙集—随机森林算法的复杂岩性识别[J]. 大庆石油地质与开发, 2017, 36(6):127-133.
|
|
ZHOU Xueqing, ZHANG Zhansong, ZHANG Chaomo, et al. Complex lithologic identification based on rough set-random forest algorism[J]. Petroleum Geology & Oilfield Development in Daqing, 2017, 36(6):127-133.
|
[23] |
刘超威, 李辉, 王泽胜, 等. 准噶尔盆地阜康凹陷二叠系上乌尔禾组油气勘探突破与启示[J]. 新疆石油地质, 2024, 45(2):139-150.
|
|
LIU Chaowei, LI Hui, WANG Zesheng, et al. Breakthrough and implication of oil and gas exploration in Permian upper Wuerhe formation in Fukang sag,Junggar basin[J]. Xinjiang Petroleum Geology, 2024, 45(2):139-150.
|
[24] |
张健, 刘楼军, 黄芸, 等. 准噶尔盆地吉木萨尔凹陷中—上二叠统沉积相特征[J]. 新疆地质, 2003, 21(4):412-414.
|
|
ZHANG Jian, LIU Loujun, HUANG Yun, et al. Sedimentary characteristics of Middle-Upper Permian in Jimusaer sag of Junggar basin[J]. Xinjiang Geology, 2003, 21(4):412-414.
|
[25] |
石军, 邹艳荣, 余江, 等. 准噶尔盆地阜康凹陷芦草沟组高有机碳页岩发育的古环境[J]. 天然气地球科学, 2018, 29(8):1138-1150.
doi: 10.11764/j.issn.1672-1926.2018.06.016
|
|
SHI Jun, ZOU Yanrong, YU Jiang, et al. Paleoenvironment of organic-rich shale from the Lucaogou formation in the Fukang sag,Junggar basin,China[J]. Natural Gas Geoscience, 2018, 29(8):1138-1150.
|
[26] |
王正和, 丁邦春, 闫剑飞, 等. 准南芦草沟组沉积特征及油气勘探前景[J]. 西安石油大学学报(自然科学版), 2016, 31(2):25-32.
|
|
WANG Zhenghe, DING Bangchun, YAN Jianfei, et al. Depositional characteristics and petroleum exploration significance of Lucaogou formation in south Junggar basin[J]. Journal of Xi’an Shiyou University(Natural Science Edition), 2016, 31(2):25-32.
|
[27] |
匡立春, 孙中春, 欧阳敏, 等. 吉木萨尔凹陷芦草沟组复杂岩性致密油储层测井岩性识别[J]. 测井技术, 2013, 37(6):638-642.
|
|
KUANG Lichun, SUN Zhongchun, OUYANG Min, et al. Complication lithology logging identification of the Lucaogou tight oil reservoir in Jimusaer depression[J]. Well Logging Technology, 2013, 37(6):638-642.
|
[28] |
王伟, 赵延伟, 毛锐, 等. 页岩油储层核磁有效孔隙度起算时间的确定:以吉木萨尔凹陷二叠系芦草沟组页岩油储层为例[J]. 石油与天然气地质, 2019, 40(3):550-557.
|
|
WANG Wei, ZHAO Yanwei, MAO Rui, et al. Determination of the starting time for measurement of NMR effective porosity in shale oil reservoir:A case study of the Permian Lucaogou shale oil reservoir,Jimusaer sag[J]. Oil & Gas Geology, 2019, 40(3):550-557.
|