石油学报 ›› 2021, Vol. 42 ›› Issue (4): 508-522.DOI: 10.7623/syxb202104008
人工智能在测井地层评价中的应用现状及前景
李宁1, 徐彬森1,2,3, 武宏亮1, 冯周1, 李雨生1, 王克文1, 刘鹏1
- 1. 中国石油勘探开发研究院 北京 100083;
2. 中国石油大学(北京)地球物理学院 北京 102249;
3. 中国石油大学(北京)人工智能学院 北京 102249
-
收稿日期:2020-02-07修回日期:2021-01-20出版日期:2021-04-25发布日期:2021-05-11 -
通讯作者:徐彬森,男,1992年7月生,2015年获北京交通大学学士学位,现为中国石油大学(北京)博士研究生,主要从事云计算、机器学习、大数据、测井、人工智能等方面的研究。Email:xbs150@163.com -
作者简介:李宁,男,1958年7月生,1982年获华东石油学院学士学位,1989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教授级高级工程师、中国工程院院士、博士生导师,主要从事测井、软件等方面研究与教学工作。Email:ln@petrochina.com.cn -
基金资助:中国石油天然气集团公司科技项目（2018D-5010-16）和中国工程院战略咨询项目（2019-XZ-17）资助。
Application status and prospects of artificial intelligence in well logging and formation evaluation
Li Ning1, Xu Binsen1,2,3, Wu Hongliang1, Feng Zhou1, Li Yusheng1, Wang Kewen1, Liu Peng1
- 1. PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China;
2. College of Geophysics, China University of Petroleum, Beijing 102249, China;
3. College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China
-
Received:2020-02-07Revised:2021-01-20Online:2021-04-25Published:2021-05-11
摘要： 测井是求取储层物性参数、发现与评价油气藏、预测油气储量的重要手段。测井技术更新换代、测井技术种类发展多样化、测井数据管理方式多元化等多重因素导致测井信息具有测量种类多、数据量大和多源异构等大数据特征。人工智能技术的快速发展为解决测井地层评价中的多解性、不确定性等难题提供了新的思路和方法，"测井＋人工智能"也是一个亟待探索的新领域。在系统回顾人工智能在测井领域的研究现状和进展基础上，重点讨论了有监督机器学习和半监督机器学习、神经网络和深度学习等人工智能技术在测井曲线重构、岩相预测和物性参数计算等测井地层评价中的应用。受样本数量有限、处理流程不完善和自我调节能力较差等因素制约，人工智能在流体研究、储层综合评价等测井领域具有较大的发展空间和广阔的应用前景。
中图分类号:
引用本文
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.
Li Ning, Xu Binsen, Wu Hongliang, Feng Zhou, Li Yusheng, Wang Kewen, Liu Peng. Application status and prospects of artificial intelligence in well logging and formation evaluation[J]. Acta Petrolei Sinica, 2021, 42(4): 508-522.
| [1] 谭廷栋.中国石油测井学科的发展及展望[J].地球物理学报,1997,40(S1):344-350. TAN Tingdong.Prospect and development for petroleum well logging science in China[J].Acta Geophysica Sinica,1997,40(S1):344-350. [2] 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. [3] WU Xuanzhi,NYLAND E.Automated stratigraphic interpretation of well-log data[J].Geophysics,1987,52(12):1665-1676. [4] DELFINER P,PEYRET O,SERRA O.Automatic determination of lithology from well logs[J].SPE Formation Evaluation,1987,2(3):303-310. [5] NIKRAVESH M,LEVEY R A,ADAMS R D.Intelligent reservoir characterization (IRESC)[C]//Proceedings of the SEG Technical Program Expanded Abstracts 1999.SEG,1999. [6] NIKRAVESH M,AMINZADEH F.Past,present and future intelligent reservoir characterization trends[J].Journal of Petroleum Science and Engineering,2001,31(2/4):67-79. [7] BERGEN K J,JOHNSON P A,DE HOOP M V,et al.Machine learning for data-driven discovery in solid earth geoscience[J].Science,2019,363(6433):eaau0323. [8] ALKINANI H H,AL-HAMEEDI A T,DUNN-NORMAN S,et al.Applications of artificial neural networks in the petroleum industry:a review[R].SPE 195072,2019. [9] XU Chicheng,MISRA S,SRINIVASAN P,et al.When petrophysics meets big data:what can machine do?[R].SPE 195068,2019. [10] 王敏生,光新军.智能钻井技术现状与发展方向[J].石油学报,2020,41(4):505-512. WANG Minsheng,GUANG Xinjun.Status and development trends of intelligent drilling technology[J].Acta Petrolei Sinica,2020,41(4):505-512. [11] 刘伟,闫娜.人工智能在石油工程领域应用及影响[J].石油科技论坛,2018,37(4):32-40. LIU Wei,YAN Na.Application and influence of artificial intelligence in petroleum engineering[J].Oil Forum,2018,37(4):32-40. [12] 肖立志.石油测井科学前沿探索文集[M].北京:石油工业出版社,2007. XIAO Lizhi.Frontiers investigation in well logging science vol.1[M].Beijing:Petroleum Industry Press,2007. [13] 贾承造.中国石油工业上游发展面临的挑战与未来科技攻关方向[J].石油学报,2020,41(12):1445-1464. JIA Chengzao.Development challenges and future scientific and technological researches in China's petroleum industry upstream[J].Acta Petrolei Sinica,2020,41(12):1445-1464. [14] 张钹.人工智能进入后深度学习时代[J].智能科学与技术学报,2019,1(1):4-6. ZHANG Bo.Artificial intelligence is entering the post deep-learning era[J].Chinese Journal of Intelligent Science and Technology,2019,1(1):4-6. [15] 尼克.人工智能简史[M].北京:人民邮电出版社,2017. NICK.A brief history of artificial intelligence[M].Beijing:Posts & Telecom Press,2017. [16] 张钹,朱军,苏航.迈向第三代人工智能[J].中国科学:信息科学,2020,50(9):1281-1302. ZHANG Bo,ZHU Jun,SU Hang.Toward the third generation of artificial intelligence[J].Scientia Sinica Informationis,2020,50(9):1281-1302. [17] HASTIE T,TIBSHIRANI R,FRIEDMAN J.The elements of statistical learning:data mining,inference,and prediction[M].New York:Springer,2009. [18] 周志华.机器学习[M].北京:清华大学出版社,2016. ZHOU Zhihua.Machine learning[M].Beijing:Tsinghua University Press,2016. [19] HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507. [20] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [21] 尤舒亚·本吉奥.人工智能中的深度结构学习[M].俞凯,译.北京:机械工业出版社,2017. BENGIO Y.Learning deep architectures for AI[M].YU Kai,trans.Beijing:China Machine Press,2017. [22] 周志华.AlphaGo专题介绍[J].自动化学报,2016,42(5):670. ZHOU Zhihua.A special introduction of AlphaGo[J].Acta Automatica Sinica,2016,42(5):670. [23] 中国国务院.新一代人工智能发展规划[M].北京:人民出版社,2017. State Council of China.Development plan of new generation artificial intelligence[M].Beijing:People's Publishing House,2017. [24] 李涓子,唐杰.2019人工智能发展报告[R].北京:清华大学-中国工程院知识智能联合研究中心,中国人工智能学会吴文俊智能科学技术奖评选基地,2019. LI Juanzi,TANG Jie.Report of artificial intelligence development 2019[R].Beijing:Tsinghua University-Chinese Academy of Engineering Joint Research Center for Knowledge and Intelligence,Wu Wenjun Intelligent Science and Technology Award Selection Base of China Artificial Intelligence Society,2019. [25] 清华-中国工程院知识智能联合研究中心,清华大学人工智能研究院知识智能研究中心,中国人工智能学会.人工智能报告2011-2020[R].北京:清华-中国工程院知识智能联合研究中心,清华大学人工智能研究院知识智能研究中心,中国人工智能学会,2021. Tsinghua Chinese Academy of Engineering Knowledge Intelligence Joint Research Center,Knowledge Intelligence Research Center,Institute of Artificial Intelligence,Tsinghua University,Chinese Society of Artificial Intelligence.Report of artificial intelligence development 2011-2020[R].Beijing:Tsinghua Chinese Academy of Engineering Knowledge Intelligence Joint Research Center,Knowledge Intelligence Research Center,Institute of Artificial Intelligence,Tsinghua University,Chinese Society of Artificial Intelligence,2021. [26] AKINNIKAWE O,LYNE S,ROBERTS J.Synthetic well log generation using machine learning techniques[R].SPE 2877021,2018. [27] 杨柳青,陈伟,查蓓.利用卷积神经网络对储层孔隙度的预测研究与应用[J].地球物理学进展,2019,34(4):1548-1555. YANG Liuqing,CHEN Wei,ZHA Pei.Prediction and application of reservoir porosity by convolutional neural network[J].Progress in Geophysics,2019,34(4):1548-1555. [28] KORJANI M,POPA A,GRIJALVA E,et al.A new approach to reservoir characterization using deep learning neural networks[R].SPE 180359,2016. [29] KARIMPOULI S,FATHIANPOUR N,ROOHI J.A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN)[J].Journal of Petroleum Science and Engineering,2010,73(3/4):227-232. [30] PARAPURAM G K,MOKHTARI M,HMIDA J B.Prediction and analysis of geomechanical properties of the upper Bakken shale utilizing artificial intelligence and data mining[R].SPE 2692746,2017. [31] 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. [32] WU P Y,JAIN V,KULKARNI M S,et al.Machine learning-based method for automated well-log processing and interpretation[R].SEG 2996973,2018. [33] 郑庆伟,王祝文,欧希阳,等.聚类分析方法在测井曲线重构中的应用[J].世界地质,2015,34(3):807-812. ZHENG Qingwei,WANG Zhuwen,OU Xiyang,et al.Application of clustering analysis in well logging curve reconstruction[J].Global Geology,2015,34(3):807-812. [34] AKKURT R,CONROY T T,PSAILA D,et al.Accelerating and enhancing petrophysical analysis with machine learning:a case study of an automated system for well log outlier detection and reconstruction[R].SPWLA 2018,2018. [35] HALL B.Facies classification using machine learning[J].The Leading Edge,2016,35(10):906-909. [36] BESTAGINI P,LIPARI V,TUBARO S.A machine learning approach to facies classification using well logs[R].SEG 17729805,2017. [37] NISHITSUJI Y,EXLEY R.Elastic impedance based facies classification using support vector machine and deep learning[J].Geophysical Prospecting,2019,67(4):1040-1054. [38] BIZE-FOREST N,LIMA L,BAINES V,et al.Using machine-learning for depositional facies prediction in a complex carbonate reservoir[R].SPWLA 2018,2018. [39] CHEN Jie,ZENG Yu.Application of machine learning in rock facies classification with physics-motivated feature augmentation[J].arXiv preprint,arXiv:1808.09856,2018. [40] 李建国,张卫东,刘冠男.深度学习在测井岩性识别中的应用[J].科技创新与应用,2015(14):21-22. LI Jianguo,ZHANG Weidong,LIU Guannan.Application of deep learning in logging lithology identification[J].Technology Innovation and Application,2015(14):21-22. [41] 李瑞.基于深度信念网络的岩性识别方法研究与实现[D].泰皇岛:燕山大学,2017. LI Rui.Research on the method for lithology identification based on deep belief network[D].Qinhuangdao:Yanshan University,2017. [42] 安鹏,曹丹平,常晓伟.基于TensorFlow进行测井岩性自动识别[C]//2017中国地球科学联合学术年会论文集(二十七)——专题54:地震物理过程、专题55:智慧地球物理.北京:中国和平音像电子出版社,2017. AN Peng,CAO Danping,CHANG Xiaowei.Automatic identification of logging lithology based on TensorFlow[C]//Proceedings of the 2017 Annual Meeting of China Joint Geosciences (27)-Topic 54:Seismic Physical Processes,Topic 55:Smart Geophysics.Beijing:China Heping Audio Visual Electronic Publishing House,2017. [43] 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 Geophysics,2008,33(3):1029-1034. [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] TSCHANNEN V,DELESCLUSE M,RODRIGUEZ M,et al.Facies classification from well logs using an inception convolutional network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017. [46] SZEGEDY C,LIU Wei,JIA Yangqing,et al.Going deeper with convolutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:1-9. [47] SHOJI D,NOGUCHI R,OTSUKI S,et al.Classification of volcanic ash particles using a convolutional neural network and probability[J].Scientific Reports,2018,8(1):8111. [48] VALENTÍN M B,BOM C R,COELHO J M,et al.A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs[J].Journal of Petroleum Science and Engineering,2019,179:474-503. [49] 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. [50] ALAUDAH Y,MICHAŁOWICZ P,ALFARRAJ M,et al.A machine-learning benchmark for facies classification[J].Interpretation,2019,7(3):SE175-SE187. [51] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:3431-3440. [52] NOH H,HONG S,HAN B.Learning deconvolution network for semantic segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Santiago,Chile:IEEE,2015:1520-1528. [53] 李冰涛,王志章,孔垂显,等.基于成像测井的裂缝智能识别新方法[J].测井技术,2019,43(3):257-262. LI Bingtao,WANG Zhizhang,KONG Chuixian,et al.A new intelligent method of fracture recognition based on imaging logging[J].Well Logging Technology,2019,43(3):257-262. [54] 吴进波.MRGC方法在储层分类及渗透率精细评价中的应用[J].海洋石油,2017,37(1):49-53. WU Jinbo.Application of reservoir classification and permeability elaborate evaluation with MRGC method[J].Offshore Oil,2017,37(1):49-53. [55] 田雨,徐洪,张兴阳,等.基于图论多分辨率聚类分析的测井岩相识别研究——以阿姆河盆地台内滩气田为例[J].应用地球物理,2016,13(4):598-607. TIAN Yu,XU Hong,ZHANG Xingyang,et al.Multi-resolution graph-based clustering analysis for lithofacies identification from well log data:case study of intraplatform bank gas fields,Amu Darya Basin[J].Applied Geophysics,2016,13(4):598-607. [56] 丁宁.一种改进的基于密度的半监督聚类及其在测井岩性识别中的应用[D].北京:中国地质大学(北京),2018. DING Ning.An improved semi supervised clustering of given density and its application in lithology identification[D].Beijing:China University of Geosciences,2018. [57] DUPONT E,ZHANG Tuanfeng,TILKE P,et al.Generating realistic geology conditioned on physical measurements with generative adversarial networks[J].arXiv preprint arXiv:1802.03065,2018. [58] MOHAGHEGH S,AREFI R,AMERI S,et al.Design and development of an artificial neural network for estimation of formation permeability[J].SPE Computer Applications,1995,7(6):151-154. [59] SAEMI M,AHMADI M,VARJANI A Y.Design of neural networks using genetic algorithm for the permeability estimation of the reservoir[J].Journal of Petroleum Science and Engineering,2007,59(1/2):97-105. [60] 马力,郑艳辉.人工神经网络预测木头油田储层孔隙度渗透率[J].河南石油,2002,16(3):15-17. MA Li,ZHENG Yanhui.Predicting reservoir porosity and permeability of Mutou oilfield with artificial neural network[J].Henan Petroleum,2002,16(3):15-17. [61] 单敬福,纪友亮,柳成志.改进人工神经网络原理对储层渗透率的预测——以北部湾盆地涠西南凹陷为例[J].石油与天然气地质,2007,28(1):106-109. SHAN Jingfu,JI Youliang,LIU Chengzhi.Prediction of reservoir permeability with improved artificial neural network principle:taking the southwest Weizhou depression in Beibuwan Basin as an example[J].Oil & Gas Geology,2007,28(1):106-109. [62] 单敬福,纪友亮,史榕,等.基于神经网络和开窗技术的储层渗透率的预测方法——以大庆萨尔图油田葡萄花油层组PI1-PI4小层砂岩为例[J].地质科学,2007,42(2):395-402. SHAN Jingfu,JI Youliang,SHI Rong,et al.Predicting method of reservoir permeability based on artificial neural net and windowing technique:an example from PI1-PI4 individual reservoir in Putaohua oilfield,Daqing[J].Chinese Journal of Geology,2007,42(2):395-402. [63] 陈蓉,王峰.基于MATLAB的BP神经网络在储层物性预测中的应用[J].测井技术,2009,33(1):75-78. CHEN Rong,WANG Feng.Application of MATLAB-based of BP neural network in reservoir parameters prediction[J].Well Logging Technology,2009,33(1):75-78. [64] 郭立新,张德梅,童茂松.基于遗传算法优化神经网络的储层渗透率预测[J].石油仪器,2009,23(2):74-77. GUO Lixin,ZHANG Demei,TONG Maosong.Predict permeability using neural network designed by genetic algorithm[J].Petroleum Instruments,2009,23(2):74-77. [65] 周金应,桂碧雯,李茂,等.基于岩控的人工神经网络在渗透率预测中的应用[J].石油学报,2010,31(6):985-988. ZHOU Jinying,GUI Biwen,LI Mao,et al.An application of the artificial neural net dominated by lithology to permeability prediction[J].Acta Petrolei Sinica,2010,31(6):985-988. [66] 周宇,魏国齐,郭和坤.一种人工智能核磁渗透率预测方法[J].江苏大学学报:自然科学版,2011,32(5):569-572. ZHOU Yu,WEI Guoqi,GUO Hekun.Permeability estimation in nuclear magnetic resonance measurements by artificial intelligence[J].Journal of Jiangsu University:Natural Science Edition,2011,32(5):569-572. [67] 董满仓,柴汝宽,辛晶,等.致密储层动态渗透率预测模型[J].陕西科技大学学报,2018,36(1):90-95. DONG Mancang,CHAI Rukuan,XIN Jing,et al.Study on prediction of dynamic permeability based on improved BP neural network[J].Journal of Shaanxi University of Science & Technology,2018,36(1):90-95. [68] 朱林奇,张冲,何小菊,等.基于改进BPNN与T2全谱的致密砂岩储层渗透率预测[J].石油物探,2017,56(5):727-734. ZHU Linqi,ZHANG Chong,HE Xiaoju,et al.Permeability Prediction of tight sandstone reservoir based on improved BPNN and T2 full-spectrum[J].Geophysical Prospecting for Petroleum,2017,56(5):727-734. [69] ZHU Linqi,ZHANG Chong,WEI Yang,et al.Permeability prediction of the tight sandstone reservoirs using hybrid intelligent algorithm and nuclear magnetic resonance logging data[J].Arabian Journal for Science and Engineering,2017,42(4):1643-1654. [70] 朱林奇,张冲,周雪晴,等.融合深度置信网络与与核极限学习机算法的核磁共振测井储层渗透率预测方法[J].计算机应用,2017,37(10):3034-3038. ZHU Linqi,ZHANG Chong,ZHOU Xueqing,et al.Nuclear magnetic resonance logging reservoir permeability prediction method based on deep belief network and kernel extreme learning machine algorithm[J].Journal of Computer Applications,2017,37(10):3034-3038. [71] WU Jinlong,YIN Xiaolong,XIAO Heng.Seeing permeability from images:fast prediction with convolutional neural networks[J].Science Bulletin,2018,63(18):1215-1222. [72] SRISUTTHIYAKORN N.Permeability prediction of 3-D binary segmented images using neural networks[C]//SEG Technical Program Expanded Abstracts 2016.SEG,2016. [73] ARAYA-POLO M,ALPAK F O,HUNTER S,et al.Deep learning-driven permeability estimation from 2D images[J].Computational Geosciences,2020,24(2):571-580. [74] GU Yufeng,BAO Zhidong,SONG Xinmin,et al.Permeability prediction for carbonate reservoir using a data-driven model comprising deep learning network,particle swarm optimization,and support vector regression:a case study of the LULA oilfield[J].Arabian Journal of Geosciences,2019,12(19):622. [75] ARIGBE O D,OYENEYIN M B,ARANA I,et al.Real-time relative permeability prediction using deep learning[J].Journal of Petroleum Exploration and Production Technology,2019,9(2):1271-1284. [76] 安鹏,曹丹平,赵宝银,等.基于LSTM循环神经网络的储层物性参数预测方法研究[J].地球物理学进展,2019,34(5):1849-1858. AN Peng,CAO Danping,ZHAO Baoyin,et al.Reservoir physical parameters prediction based on LSTM recurrent neural network[J].Progress in Geophysics,2019,34(5):1849-1858. [77] ORUGANTI Y D,YUAN Peng,INANC F,et al.Role of machine learning in building models for gas saturation prediction[R].SPWLA 2019,2019. [78] BENNIS M,TORRES-VERDÍN C.Estimation of dynamic petrophysical properties from multiple well logs using machine learning and unsupervised rock classification[R].SPWLA 2019,2019. [79] ARKALGUD R,MCDONALD A,CROMBIE D.Domain transfer analysis-a robust new method for petrophysical analysis[R].SPWLA 2019,2019. [80] VENKATARAMANAN L,EVIRGEN N,ALLEN D F,et al.An unsupervised learning algorithm to compute fluid volumes from NMR T1-T2 logs in unconventional reservoirs[J].Petrophysics,2018,59(5):617-632. [81] DUARTE S B,DE JESUS C M,DA SILVA V F,et al.Artificial intelligence use to predict severe fluid losses in pre-salt carbonates[R].SPWLA 2018,2018. [82] DAI Bin,JONES C,PRICE J,et al.Auto-navigation of optimal formation pressure testing locations by machine learning methods[R].SPWLA 2019,2019. [83] MARKET J,RAMOS L,HARRIS N,et al.Acoustic fracture characterisation-intelligent interpretation[R].SPWLA 2017,2017. [84] BAYRAKTAR Z,OMERAGIC D,CHEN Yonghua.Quantitative interpretation of oil-base mud microresistivity imager via artificial neural networks[R].SPWLA 2019,2019. [85] GUPTA K D,VALLEGA V,MANIAR H,et al.A deep-learning approach for borehole image interpretation[R].SPWLA 2019,2019. [86] JAIN V,WU P Y,AKKURT R,et al.Class-based machine learning for next-generation wellbore data processing and interpretation[R].SPWLA 2019,2019. [87] LE T,LIANG Lin,ZIMMERMANN T,et al.A machine-learning framework for automating well-log depth matching[J].Petrophysics,2019,60(5):585-595. [88] PEYRET A P,AMBÍA J,TORRES-VERDÍN C,et al.Automatic interpretation of well logs with lithology-specific deep-learning methods[C].SPWLA 2019,2019. [89] SHAO Wei,CHEN Songhua,EID M,et al.Carbonate log interpretation models based on machine learning techniques[R].SPWLA 2019,2019. [90] BIGONI F,PIRRONE M,PINELLI F,et al.A multi-scale path for the characterization of heterogeneous karst carbonates:how log-to-seismic machine learning can optimize hydrocarbon production[R].SPWLA 2019,2019. [91] GAILLOT P,LEWANDOWSKI J,NURSAIDOVA R.Uncertainty analysis in formation evaluation:rationale,methods and examples[R].SPWLA 2019,2019. [92] ESLINGER E,BOYLE F,CURTIS A A.Leveraging probabilisitic multivariate clustering analyses of well logs to identify "sweet spot" intervals in heterogeneous conventional and unconventional reservoirs[R].SPWLA 2019,2019. [93] JIN Yuchen,SHEN Qiuyang,WU Xuqing,et al.A physics-driven deep-learning network for solving nonlinear inverse problems[J].Petrophysics,2020,61(1):86-98. [94] LIANG Lin,LEI Ting.Machine learning-enabled automatic sonic shear processing[R].SPWLA 5076,2020. [95] EMELYANOVA I,PEYAUD J B,DANCE T,et al.Detecting specific facies in well-log data sets using knowledge-driven hierarchical clustering[J].Petrophysics,2020,61(4):383-400. [96] GONZALEZ A,HEIDARI Z,LOPEZ O.Integration of 3D volumetric CT-scan image data with conventional well logs for detection of petrophysical rock classes[R].SPWLA 5071,2020. [97] GONZALEZ A,KANYAN L,HEIDARI Z,et al.Integrated multiphysics workflow for automatic rock classification and formation evaluation using multiscale image analysis and conventional well logs[J].Petrophysics,2020,61(5):495-518. [98] DENG Tianqi,AMBÍA J,TORRES-VERDÍN C.Bayesian method for rapid multi-well interpretation of well logs and core data in unconventional formations[R].SPWLA 5040,2020. [99] Koeshidayatullah A,Morsilli M,Lehrmann D J,et al.Fully automated carbonate petrography using deep convolutional neural networks[J]. Marine and Petroleum Geology,2020,122:104687. [100] Pattnaik S,CHEN Songhua,WEI Shao,et al.Automating microfacies analysis of petrographic images[R].SPWLA 5074,2020. [101] 原野,李宁,王才志,等.多井多维异构数据交会图增维显示分析方法[J].石油学报,2020,41(9):1100-1108. YUAN Ye,LI Ning,WANG Caizhi,et al.A visualization and analysis method of augmented dimension of multi-well and multi-dimensional heterogeneous data crossplot[J].Acta Petrolei Sinica,2020,41(9):1100-1108. |
| [1] | 李祯, 郭奇, 卜亚辉, 胡慧芳. 基于深度学习的饱和度场样本库建立及预测[J]. 石油学报, 2024, 45(4): 698-707. |
| [2] | 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. |
| [3] | 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. |
| [4] | 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. |
| [5] | 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. |
| [6] | 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. |
| [7] | 王敏生, 光新军. 智能钻井技术现状与发展方向[J]. 石油学报, 2020, 41(4): 505-512. |
| [8] | 王以法. 新型智能钻头的设计[J]. 石油学报, 2003, 24(1): 92-95. |
| [9] | 王以法. 人工智能钻井实时专家控制系统研究[J]. 石油学报, 2001, 22(2): 83-86. |
| [10] | 吴新根, 葛家理. 应用人工神经网络识别碳酸盐岩相[J]. 石油学报, 1996, 17(4): 50-54. |
| [11] | 张绍槐, 何华灿, 李琪, 郭建明. 石油钻井信息技术的智能化研究[J]. 石油学报, 1996, 17(4): 114-119. |
| [12] | 姜效典, 李巍然. 应用瓦尔什变换和人工神经网络划分岩层和岩性识别[J]. 石油学报, 1994, 15(4): 17-22. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||