•  
  •  
 

Coal Geology & Exploration

Authors

YANG Yulong, School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, ChinaFollow
CAO Weihua, School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, ChinaFollow
GAN Chao, School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
LI Yupeng, School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
WU Min, School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China

Abstract

Significance As the new round of national exploration & development planning continues, resource exploration is advancing toward the deep Earth. However, deep strata exhibit diverse rock types, which complicate the measurement of rock mechanical parameters. Furthermore, the harsh environments of these strata, characterized by high temperatures, high pressures, and high in-situ stress, are prone to induce downhole accidents like drilling tool failure, wellbore collapse, loss of circulation, and well kicks, posing challenges to geological drilling. Advances Aiming at the perception and modeling of complex geological environments, this study reviews the existing studies on the modeling of stratigraphic characteristic parameters from the perspective of formation drillability and formation pressure, aiming to provide guidance for the technique adjustment and efficiency optimization of geological drilling processes based on these two key characteristic parameters. To satisfy the demands for safe and efficient geological drilling, this study explores the advances in research on the safety early warning of the geological drilling process from two perspectives: wellbore stability assessment and downhole failure monitoring. The safety early warning technology allows drillers to promptly find and identify downhole accidents and, accordingly, eliminate potential safety hazards in advance. Prospects Under the more complex and harsh geologic conditions of deep strata compared to those of shallow ones, the models of stratigraphic characteristic parameters will play a more significant role in the geological drilling process, and the safety early warning technology will act as the core technology in the next generation of intelligent geological drilling equipment. In the future, it is necessary to build an intelligent geological exploration system with the geological exploration data platform as the core, make data play a key role in the whole process of geological exploration and mineral exploitation, and promote the application of artificial intelligence in the optimization of the geological drilling process, the perception of stratigraphic environments, and prospecting predictions. The purpose is to ensure safe and efficient geological drilling.

Keywords

geological drilling, geological drilling process, environmental perception, modeling of a stratigraphic characteristic parameters, safety early warning, geological exploration data platform

DOI

10.12363/issn.1001-1986.24.05.0341

Reference

[1] 侯梅芳. 碳中和目标下中国能源转型和能源安全的现状、挑战与对策[J]. 西南石油大学学报(自然科学版),2023,45(2):1−10.

HOU Meifang. Current situation,challenges and countermeasures of china’s energytrans formation and energy security under the goal of carbon neutrality[J]. Journal of Southwest Petroleum University (Science & Technology Edition),2023,45(2):1−10.

[2] 中华人民共和国自然资源部. 中国矿产资源报告2023[EB/OL]. (2023-10-30) [2024-5-10]. https://m.mnr.gov.cn/dt/ywbb/202310/t20231030_2804785.html.

[3] 中华人民共和国自然资源部. 2023年中国自然资源公报[EB/OL]. (2024-02-29)[2024-5-10]. http://gi.mnr.gov.cn/202402/t20240229_2838490.html.

[4] LÜ Qingtian,YAN Jiayong,CHEN Xuanhua,et al. Progress of deep geological survey project under the China geological survey[J]. China Geology,2020,3(1):153−172.

[5] 吴敏. 复杂地质钻进过程智能控制[M]. 北京:科学出版社,2022.

[6] 姚宁平,吴敏,陈略峰,等. 煤矿坑道钻进过程智能优化与控制技术[J]. 煤田地质与勘探,2023,51(9):1−9.

YAO Ningping,WU Min,CHEN Lüefeng,et al. Intelligent optimization and control technology for drilling process of coal mine tunnels[J]. Coal Geology & Exploration,2023,51(9):1−9.

[7] LI Gensheng,SONG Xianzhi,TIAN Shouceng,et al. Intelligent drilling and completion:A review[J]. Engineering,2022,18:33−48.

[8] ZHU Haiyan,DENG Jingen,XIE Yuhong,et al. Rock mechanics characteristic of complex formation and faster drilling techniques in western South China Sea oilfields[J]. Ocean Engineering,2012,44:33−45.

[9] FENG Xianda,JIMENEZ R. Bayesian prediction of elastic modulus of intact rocks using their uniaxial compressive strength[J]. Engineering Geology,2014,173:32−40.

[10] PISHBIN M,FATHIANPOUR N,MOKHTARI A R. Uniaxial compressive strength spatial estimation using different interpolation techniques[J]. International Journal of Rock Mechanics and Mining Sciences,2016,89:136−150.

[11] SINGH R,KAINTHOLA A,SINGH T N. Estimation of elastic constant of rocks using an ANFIS approach[J]. Applied Soft Computing,2012,12(1):40−45.

[12] MA Hai. Formation drillability prediction based on multi-source information fusion[J]. Journal of Petroleum Science and Engineering,2011,78(2):438−446.

[13] GAN Chao,CAO Weihua,LIU Kangzhi,et al. A new spatial modeling method for 3D formation drillability field using fuzzy c-means clustering and random forest[J]. Journal of Petroleum Science and Engineering,2021,200:108371.

[14] 陆承达,甘超,陈略峰,等. 地质钻进过程智能控制研究进展与发展前景[J]. 煤田地质与勘探,2023,51(9):31−43.

LU Chengda,GAN Chao,CHEN Lüefeng,et al. Development and prospect of intelligent control of geological drilling process[J]. Coal Geology & Exploration,2023,51(9):31−43.

[15] 纪慧,朱亮,楼一珊,等. 南海深部温压地层岩石可钻性评价及应用[J]. 科学技术与工程,2022,22(33):14707−14713.

JI Hui,ZHU Liang,LOU Yishan,et al. Evaluation and application of rock drillability of deep thermobaric strata in the South China Sea[J]. Science Technology and Engineering,2022,22(33):14707−14713.

[16] 杨磊,杨本高,刘军军,等. 激光热裂砂岩可钻性及力学参数特征研究[J]. 煤田地质与勘探,2023,51(8):171−180.

YANG Lei,YANG Bengao,LIU Junjun,et al. Drillability and mechanical parameters of laser hot cracking sandstones[J]. Coal Geology & Exploration,2023,51(8):171−180.

[17] CAYEUX E,SHOR R,AMBRUS A,et al. From shallow horizontal drilling to ERD wells:How scale affects drillability and the management of drilling incidents[J]. Journal of Petroleum Science and Engineering,2018,160:91−105.

[18] GAN Chao,CAO Weihua,WU Min,et al. Intelligent nadaboost-ELM modeling method for formation drillability using well logging data[J]. Journal of Advanced Computational Intelligence and Intelligent Informatics,2016,20(7):1103−1111.

[19] GAN Chao,CAO Weihua,WU Min,et al. An online modeling method for formation drillability based on OS-nadaboost-ELM algorithm in deep drilling process[J]. IFAC-PapersOnLine,2017,50:12886−12891.

[20] GAN Chao,CAO Weihua,WU Min,et al. Two-level intelligent modeling method for the rate of penetration in complex geological drilling process[J]. Applied Soft Computing,2019,80:592−602.

[21] 甘超,汪祥,王鲁朝,等. 基于区域多井数据优选与模型预训练的深部地质钻探过程钻速动态预测方法[J]. 钻探工程,2023(4):1−8.

GAN Chao,WANG Xiang,WANG Luzhao,et al. Dynamic prediction method of rate of penetration (ROP) in deep geological drilling process based on regional multi-well data optimization and model pre-training[J]. Drilling Engineering,2023(4):1−8.

[22] MUKHERJEE B,SAIN K. Vertical lithological proxy using statistical and artificial intelligence approach:A case study from Krishna-Godavari Basin,offshore India[J]. Marine Geophysical Research,2021,42:3.

[23] ZHANG X,ZHAI Y H,XUE C J,et al. A study of the distribution of formation drillability[J]. Petroleum Science and Technology,2011,29(2):149−159.

[24] ZHANG Yi,DUAN Menglan,SUN Tengfei,et al. Application of fractal theory in drillability evaluation for off-shore oilfields[J]. Chemistry and Technology of Fuels and Oils,2018,54(4):509−512.

[25] ZHANG Yi,DUAN Menglan,KONG Xiangji,et al. Study of drillability evaluation in deep formations using the Kriging interpolation method[J]. Chemistry and Technology of Fuels and Oils,2018,54(3):382−385.

[26] GAN Chao,CAO Weihua,LIU Kangzhi,et al. Spatial estimation for 3D formation drillability field:A new modeling framework[J]. Journal of Natural Gas Science and Engineering,2020,84:103628.

[27] PRANKADA M,YADAV K,SIRCAR A. Analysis of wellbore stability by pore pressure prediction using seismic velocity[J]. Energy Geoscience,2021,2(4):219−228.

[28] QIN Su,XU Tao,ZHOU Wanhuan. Predicting pore-water pressure in front of a TBM using a deep learning approach[J]. International Journal of Geomechanics,2021,21(8):04021140.

[29] KHALEDI K,HAMDI P,WINHAUSEN L,et al. Unloading induced absolute negative pore pressures in a low permeable clay shale[J]. Engineering Geology,2021,295:106451.

[30] NWONODI R I,DOSUNMU A. Analysis of a porosity-based pore pressure model derived from the effective vertical stress[J]. Journal of Petroleum Science and Engineering,2021,204:108727.

[31] LI Chong,ZHAN Linsen,LU Hailong. Mechanisms for overpressure development in marine sediments[J]. Journal of Marine Science and Engineering,2022,10(4):490.

[32] 周鹏高. 欠压实作用下地层异常压力定量评价方法及应用[J]. 特种油气藏,2023,30(6):23−30.

ZHOU Penggao. Quantitative evaluation method of anomalous formation pressure under the effect of undercompaction and its application[J]. Special Oil & Gas Reservoirs,2023,30(6):23−30.

[33] LI Wei,CHEN Zhuxin,HUANG Pinghui,et al. Formation of overpressure system and its relationship with the distribution of large gas fields in typical foreland basins in central and Western China[J]. Petroleum Exploration and Development,2021,48(3):625−640.

[34] LIU Jingdong,LIU Tao,LIU Hua,et al. Overpressure caused by hydrocarbon generation in the organic-rich shales of the Ordos Basin[J]. Marine and Petroleum Geology,2021,134:105349.

[35] PEREZ-SILVA A,KANEKO Y,SAVAGE M,et al. Characteristics of slow slip events explained by rate-strengthening faults subject to periodic pore fluid pressure changes[J]. Journal of Geophysical Research:Solid Earth,2023,128(6):1−25.

[36] MENG Qingfeng,HAO Fang,TIAN Jinqiang. Origins of non-tectonic fractures in shale[J]. Earth-Science Reviews,2021,222:103825.

[37] AGBASI O E,SEN S,INYANG N J,et al. Assessment of pore pressure,wellbore failure and reservoir stability in the Gabo field,Niger Delta,Nigeria:Implications for drilling and reservoir management[J]. Journal of African Earth Sciences,2021,173:104038.

[38] EATON B A. The effect of overburden stress on geopressure prediction from well logs[J]. Journal of Petroleum Technology,1972,24(8):929−934.

[39] BOWERS G L. Pore pressure estimation from velocity data:Accounting for overpressure mechanisms besides undercompaction[J]. SPE Drilling & Completion,1995,10(2):89−95.

[40] FARSI M,MOHAMADIAN N,GHORBANI H,et al. Predicting formation pore-pressure from well-log data with hybrid machine-learning optimization algorithms[J]. Natural Resources Research,2021,30(5):3455−3481.

[41] HUTOMO P S,ROSID M S,HAIDAR M W. Pore pressure prediction using Eaton and neural network method in carbonate field “X” based on seismic data[J]. IOP Conference Series:Materials Science and Engineering,2019,546(3):032017.

[42] CZERNIAK M. RhoVe method:A new empirical pore pressure transform[J]. Marine and Petroleum Geology,2017,86:343−366.

[43] RAMU C,SUNKARA S L,RAMU R,et al. An ANN-based identification of geological features using multi-attributes:A case study from Krishna-Godavari Basin,India[J]. Arabian Journal of Geosciences,2021,14(4):299.

[44] 赵军,曹强,叶加仁,等. 基于地震速度预测南堡凹陷中深层地层压力[J]. 油气地质与采收率,2016,23(4):34−40.

ZHAO Jun,CAO Qiang,YE Jiaren,et al. Prediction of overpressure distribution in mid-deep strata of Nanpu Sag based on seismic velocity[J]. Petroleum Geology and Recovery Efficiency,2016,23(4):34−40.

[45] 曹园,邓金根,蔚宝华. WZ12-1油田流沙港组一段异常高压预测[J]. 特种油气藏,2013,20(6):99−101.

CAO Yuan,DENG Jingen,YU Baohua. Prediction and analysis of abnormal high pressure reservoir in the first member of Liushagang Formation in south WZ12-1 oilfield[J]. Special Oil & Gas Reservoirs,2013,20(6):99−101.

[46] SATTI I A,GHOSH D,YUSOFF W I,et al. Origin of overpressure in a field in the Southwestern Malay Basin[J]. SPE Drilling & Completion,2015,30(3):198−211.

[47] CHEN Xi,CAO Weihua,GAN Chao,et al. A hybrid spatial model based on identified conditions for 3D pore pressure estimation[J]. Journal of Natural Gas Science and Engineering,2022,100:104448.

[48] BAOUCHE R,SEN S,GANGULI S S. Pore pressure and in situ stress magnitudes in the Bhiret Hammou hydrocarbon field,Berkine Basin,Algeria[J]. Journal of African Earth Sciences,2020,171:103945.

[49] RADWAN A E,ABUDEIF A M,ATTIA M M,et al. Geopressure evaluation using integrated basin modelling,well-logging and reservoir data analysis in the northern part of the Badri oil field,Gulf of Suez,Egypt[J]. Journal of African Earth Sciences,2020,162:103743.

[50] 戴瑞. 顺南地区碳酸盐岩地层压力分析研究[D]. 北京:中国石油大学(北京),2017.

DAI Rui. Analysis and study on carbonate formation pressure in Shunnan area[D]. Beijing:China University of Petroleum (Beijing),2017.

[51] 刘金水. 西湖凹陷平湖构造带地层压力特征及与油气分布的关系[J]. 成都理工大学学报(自然科学版),2015,42(1):60−69.

LIU Jinshui. Characteristics of formation pressure and their relationship with hydrocarbon distribution in Pinghu tectonic belt of Xihu Sag,East China Sea[J]. Journal of Chengdu University of Technology (Science & Technology Edition),2015,42(1):60−69.

[52] KARMAKAR M,MAITI S. Short term memory efficient pore pressure prediction via Bayesian neural networks at Bering Sea slope of IODP expedition 323[J]. Measurement,2019,135:852−868.

[53] ABDULMALEK A S,ELKATATNY S,ABDULRAHEEM A,et al. Pore pressure prediction while drilling using fuzzy logic[C]//All Days. Dammam,Saudi Arabia. SPE,2018:1–12.

[54] 李红. Dc指数随钻监测地层压力的应用分析[J]. 海洋石油,2017,37(3):43−48.

LI Hong. Application and analysis of monitoring formation pressure while drilling with Dc index[J]. Offshore Oil,2017,37(3):43−48.

[55] ROY D K,RAY G K,BISWAS A K. Overview of overpressure in Bengal Basin,India[J]. Journal of the Geological Society of India,2010,75(4):644−660.

[56] OLORUNTOBI O,BUTT S. Energy-based formation pressure prediction[J]. Journal of Petroleum Science and Engineering,2019,173:955−964.

[57] OLORUNTOBI O,ADEDIGBA S,KHAN F,et al. Overpressure prediction using the hydro-rotary specific energy concept[J]. Journal of Natural Gas Science and Engineering,2018,55:243−253.

[58] 时梦璇,刘之的,杨学峰,等. 地层孔隙压力地球物理测井预测技术综述及展望[J]. 地球物理学进展,2020,35(5):1845−1853.

SHI Mengxuan,LIU Zhidi,YANG Xuefeng,et al. Review and prospect prediction technology for formation pore pressure by geophysical well logging[J]. Progress in Geophysics,2020,35(5):1845−1853.

[59] 王胜建,迟焕鹏,庞飞,等. 黔北正安地区页岩气钻探工程难点与对策研究[J]. 地质与勘探,2023,59(1):162−169.

WANG Shengjian,CHI Huanpeng,PANG Fei,et al. Challenges and solutions of drilling engineering in the Zheng’an area of northern Guizhou Province[J]. Geology and Exploration,2023,59(1):162−169.

[60] 胡郁乐,胡晨,张恒春,等. 钻头泥包原因分析及松科二井防泥包钻井液的应用[J]. 煤田地质与勘探,2020,48(5):254−261.

HU Yule,HU Chen,ZHANG Hengchun,et al. Analysis of bit balling and application of the balling-preventing drilling fluid in well Songke-2[J]. Coal Geology & Exploration,2020,48(5):254−261.

[61] 赵博,郏志刚,陈颖超. 钻井事故预防与处理[M]. 2版. 北京:石油工业出版社,2020.

[62] WADA R,KANEKO T,OZAKI M,et al. Longitudinal natural vibration of ultra-long drill string during offshore drilling[J]. Ocean Engineering,2018,156:1−13.

[63] 陈茜. 复杂地质环境井壁稳定性特征参数智能建模[D]. 武汉:中国地质大学,2022.

CHEN Qian. Intelligent modeling of characteristic parameters of wellbore stability in complex geological environment[D]. Wuhan:China University of Geosciences,2022.

[64] BAOUCHE R,SEN S,SADAOUI M,et al. Characterization of pore pressure,fracture pressure,shear failure and its implications for drilling,wellbore stability and completion design:A case study from the Takouazet field,Illizi Basin,Algeria[J]. Marine and Petroleum Geology,2020,120:104510.

[65] 王旭升,谢永桦,陈崇希. 潜水稳定井流的剖面二维数值模拟方法[J]. 地质科技通报,2023(4):27−36.

WANG Xusheng,XIE Yonghua,CHEN Chongxi. Sectional 2D numerical modelling method for steady state well-flow in an unconfined aquifer[J]. Bulletin of Geological Science and Technology,2023(4):27−36.

[66] CHEN Xi,CAO Weihua,GAN Chao,et al. Semi-supervised support vector regression based on data similarity and its application to rock-mechanics parameters estimation[J]. Engineering Applications of Artificial Intelligence,2021,104:104317.

[67] 舒红林,仇凯斌,李庆飞,等. 页岩气地质力学特征评价方法:中国南方海相强改造区山地页岩地质力学特征[J]. 天然气工业,2021,41(增刊1):1−13.

SHU Honglin,QIU Kaibin,LI Qingfei,et al. A method for evaluating the geomechanical characteristics of shale gas:The geomechanical characteristics of the mountain shale in the intensively reworked marine area of South China[J]. Natural Gas Industry,2021,41(Sup.1):1−13.

[68] ZHANG Zongxian,HOU Defeng,ALADEJARE A. Empirical equations between characteristic impedance and mechanical properties of rocks[J]. Journal of Rock Mechanics and Geotechnical Engineering,2020,12(5):975−983.

[69] ANYA A,EMADI H,WATSON M. An empirical model for calculating uniaxial compressive strength of oil well cements from ultrasonic pulse transit time measurements[J]. Journal of Petroleum Science and Engineering,2019,183:106387.

[70] HE Mingming,ZHANG Zhiqiang,REN Jie,et al. Deep convolutional neural network for fast determination of the rock strength parameters using drilling data[J]. International Journal of Rock Mechanics and Mining Sciences,2019,123:104084.

[71] ANEMANGELY M,RAMEZANZADEH A,MOHAMMADI BEHBOUD M. Geomechanical parameter estimation from mechanical specific energy using artificial intelligence[J]. Journal of Petroleum Science and Engineering,2019,175:407−429.

[72] KAINTHOLA A,SINGH P K,VERMA D,et al. Prediction of strength parameters of Himalayan rocks:A statistical and ANFIS approach[J]. Geotechnical and Geological Engineering,2015,33(5):1255−1278.

[73] TARIQ Z,MAHMOUD M,ABDULRAHEEM A. Core log integration:A hybrid intelligent data-driven solution to improve elastic parameter prediction[J]. Neural Computing and Applications,2019,31(12):8561−8581.

[74] ZHANG Qiangui,RAN Jiawei,FAN Xiangyu,et al. Mechanical properties of basalt,tuff and breccia in the Permian system of Sichuan Basin after water absorption–implications for wellbore stability analysis[J]. Acta Geotechnica,2023,18(4):2059−2080.

[75] 金衍,薄克浩,张亚洲,等. 深层硬脆性泥页岩井壁稳定力学化学耦合研究进展与思考[J]. 石油钻探技术,2023,51(4):159−169.

JIN Yan,BO Kehao,ZHANG Yazhou,et al. Advancements and considerations of chemo-mechanical coupling for wellbore stability in deep hard brittle shale[J]. Petroleum Drilling Techniques,2023,51(4):159−169.

[76] IBRAHIM A. A review of mathematical modelling approaches to tackling wellbore instability in shale formations[J]. Journal of Natural Gas Science and Engineering,2021,89:103870.

[77] 李治衡,刘海龙,庹海洋,等. 渤海浅部砂泥岩地层井壁蠕变缩径研究[J]. 石油机械,2019,47(12):44−49.

LI Zhiheng,LIU Hailong,TUO Haiyang,et al. Study on creep and shrinkage of borehole in shallow sandstone and mudstone formation in the Bohai Sea[J]. China Petroleum Machinery,2019,47(12):44−49.

[78] DONG Zhikai,LI Yinping,LI Haoran,et al. Experimental study on the influence of temperature on rock salt creep[J]. Rock Mechanics and Rock Engineering,2023,56(5):3499−3518.

[79] LYU Cheng,LIU Jianfeng,REN Yi,et al. Study on very long-term creep tests and nonlinear creep-damage constitutive model of salt rock[J]. International Journal of Rock Mechanics and Mining Sciences,2021,146:104873.

[80] 李浩然,徐壮,魏群,等. 盐岩高温三轴蠕变损伤破裂机制试验研究[J]. 岩石力学与工程学报,2023,42(12):2945−2956.

LI Haoran,XU Zhuang,WEI Qun,et al. Experimental study on salt rock creep damage rupture mechanism in high temperature triaxial conditions[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(12):2945−2956.

[81] ZHAO Bin,LI Yong,ZHANG Hui,et al. Computing safety drilling fluid density of ultra-deep rock salt formation[J]. Bulletin of Engineering Geology and the Environment,2022,81(10):453.

[82] WEIJERMARS R,WANG J,PHAM T. Borehole failure mechanisms in naturally fractured formations[J]. Rock Mechanics and Rock Engineering,2022,55(5):3001−3022.

[83] 舒曼,赵明琨,许明标. 涪陵页岩气田油基钻井液随钻堵漏技术[J]. 石油钻探技术,2017,45(3):21−26.

SHU Man,ZHAO Mingkun,XU Mingbiao. Plugging while drilling technology using oil-based drilling fluid in Fuling shale gas field[J]. Petroleum Drilling Techniques,2017,45(3):21−26.

[84] 范翔宇,蒙承,张千贵,等. 超深地层井壁失稳理论与控制技术研究进展[J]. 天然气工业,2024,44(1):159−176.

FAN Xiangyu,MENG Cheng,ZHANG Qiangui,et al. Research progress in the evaluation theory and control technology of wellbore instability in ultra-deep strata[J]. Natural Gas Industry,2024,44(1):159−176.

[85] JIANG Hailong,LIU Gonghui,LI Jun,et al. Drilling fault classification based on pressure and flowrate responses via ensemble classifier in managed pressure drilling[J]. Journal of Petroleum Science and Engineering,2020,190:107126.

[86] LIU Kaixin,XIAO Anfeng,ZHANG Peng,et al. Study on mechanical response of steel pipe jacking considering the effect of pipe sticking[J]. Tunnelling and Underground Space Technology,2022,127:104617.

[87] 朱硕,宋先知,李根生,等. 钻柱摩阻扭矩智能实时分析与卡钻趋势预测[J]. 石油钻采工艺,2021,43(4):428−435.

ZHU Shuo,SONG Xianzhi,LI Gensheng,et al. Intelligent real-time drag and torque analysis and sticking trend prediction of drill string[J]. Oil Drilling & Production Technology,2021,43(4):428−435.

[88] YU Zhiming,ZENG Dezhi,HU Shurui,et al. The failure patterns and analysis process of drill pipes in oil and gas well:A case study of fracture S135 drill pipe[J]. Engineering Failure Analysis,2022,138:106171.

[89] SHEN Dan,TONG Ke,FAN Zhihai,et al. Failure analysis of S135 drill pipe body fracture in a well[J]. Engineering Failure Analysis,2023,145:106998.

[90] YANG Jingbin,SUN Jinsheng,BAI Yingrui,et al. Status and prospect of drilling fluid loss and lost circulation control technology in fractured formation[J]. Gels,2022,8(5):260.

[91] PU Lei,XU Peng,XU Mingbiao,et al. Lost circulation materials for deep and ultra-deep wells:A review[J]. Journal of Petroleum Science and Engineering,2022,214:110404.

[92] WANG Chao,LIU Gonghui,LI Jun,et al. Downhole mud loss transient simulation and detection with downhole dual measurement points[J]. Journal of Petroleum Science and Engineering,2021,198:108184.

[93] 张正,赖旭芝,陆承达,等. 基于贝叶斯网络的钻进过程井漏井涌事故预警[J]. 探矿工程(岩土钻掘工程),2020,47(4):114−121.

ZHANG Zheng,LAI Xuzhi,LU Chengda,et al. Lost circulation and kick accidents warning based on Bayesian network for the drilling process[J]. Drilling Engineering,2020,47(4):114−121.

[94] JIANG Hailong,LIU Gonghui,LI Jun,et al. An innovative diagnosis method for lost circulation with unscented Kalman filter[J]. Journal of Petroleum Science and Engineering,2018,166:731−738.

[95] GENG Zhi,WANG Hanqing,FAN Meng,et al. Predicting seismic-based risk of lost circulation using machine learning[J]. Journal of Petroleum Science and Engineering,2019,176:679−688.

[96] LI Yupeng,CAO Weihua,HU Wenkai,et al. Diagnosis of downhole incidents for geological drilling processes using multi-time scale feature extraction and probabilistic neural networks[J]. Process Safety and Environmental Protection,2020,137:106−115.

[97] ZHANG Zheng,LAI Xuzhi,DU Sheng,et al. Early warning of loss and kick for drilling process based on sparse autoencoder with multivariate time series[J]. IEEE Transactions on Industrial Informatics,2023,19(11):11019−11029.

[98] CHEN Xin,HE Miao,XU Mingbiao,et al. Early gas kick detection-inversion-control integrated system:The significance of applications of managed pressure drilling:A review[J]. Geoenergy Science and Engineering,2023,229:212134.

[99] LI Yupeng,CAO Weihua,HU Wenkai,et al. Incipient fault detection for geological drilling processes using multivariate generalized Gaussian distributions and Kullback–Leibler divergence[J]. Control Engineering Practice,2021,117:104937.

[100] LI Yupeng,CAO Weihua,GOPALUNI R B,et al. False alarm reduction in drilling process monitoring using virtual sample generation and qualitative trend analysis[J]. Control Engineering Practice,2023,133:105457.

[101] LIANG Haibo,ZOU Jialing,LI Zhiling,et al. Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm[J]. Future Generation Computer Systems,2019,95:454−466.

[102] LI Yupeng,CAO Weihua,HU Wenkai,et al. Detection of downhole incidents for complex geological drilling processes using amplitude change detection and dynamic time warping[J]. Journal of Process Control,2021,102:44−53.

[103] LI Yupeng,CAO Weihua,GOPALUNI R B,et al. Drilling process monitoring based on operation mode recognition and dynamic feature extraction[J]. IEEE Transactions on Industrial Electronics,2024,71(7):7876−7885.

[104] 范海鹏. 基于数据特征解析的复杂地质钻进过程智能状态监测与评价方法[D]. 武汉:中国地质大学,2023.

FAN Haipeng. Intelligent condition monitoring and evaluation method of complex geological drilling process based on data feature analysis[D]. Wuhan:China University of Geosciences,2023.

[105] 李根生,宋先知,田守嶒. 智能钻井技术研究现状及发展趋势[J]. 石油钻探技术,2020,48(1):1−8.

LI Gensheng,SONG Xianzhi,TIAN Shouceng. Intelligent drilling technology research status and development trends[J]. Petroleum Drilling Techniques,2020,48(1):1−8.

[106] 胡永建,黄衍福,石林. 高频磁耦合有缆钻杆信道建模与仿真分析[J]. 石油学报,2018,39(11):1292−1298.

HU Yongjian,HUANG Yanfu,SHI Lin. Channel modeling and simulation of high-frequency magnetic coupling wired drill pipe[J]. Acta Petrolei Sinica,2018,39(11):1292−1298.

[107] AL-HAMEEDI A T T,ALKINANI H H,DUNN-NORMAN S,et al. Real-time lost circulation estimation and mitigation[J]. Egyptian Journal of Petroleum,2018,27(4):1227−1234.

[108] YANG Aoxue,LU Chengda,YU Wanke,et al. Data augmentation considering distribution discrepancy for fault diagnosis of drilling process with limited samples[J]. IEEE Transactions on Industrial Electronics,2023,70(11):11774−11783.

[109] WANG Chuan,MA Jiajun,JIN Hao,et al. ACGAN and BN based method for downhole incident diagnosis during the drilling process with small sample data size[J]. Ocean Engineering,2022,256:111516.

[110] ZHOU Jing,KRSTIC M. Adaptive predictor control for stabilizing pressure in a managed pressure drilling system under time-delay[J]. Journal of Process Control,2016,40:106−118.

[111] 甘超,曹卫华,王鲁朝,等. 深部地质钻探钻进过程流式大数据分析与动态预处理:以辽宁丹东3000 m科学钻探工程为例[J]. 钻探工程,2022,49(4):1−7.

GAN Chao,CAO Weihua,WANG Luchao,et al. Streaming big data analysis and dynamic pre-processing in deep geological drilling process:A case study on the 3000 m scientific drilling project in Dandong,Liaoning Province[J]. Drilling Engineering,2022,49(4):1−7.

[112] 李泉新,石智军,田宏亮,等. 我国煤矿区钻探技术装备研究进展[J]. 煤田地质与勘探,2019,47(2):1−6.

LI Quanxin,SHI Zhijun,TIAN Hongliang,et al. Progress in the research on drilling technology and equipment in coal mining areas of China[J]. Coal Geology & Exploration,2019,47(2):1−6.

[113] 徐凤银,闫霞,林振盘,等. 我国煤层气高效开发关键技术研究进展与发展方向[J]. 煤田地质与勘探,2022,50(3):1−14.

XU Fengyin,YAN Xia,LIN Zhenpan,et al. Research progress and development direction of key technologies for efficient coalbed methane development in China[J]. Coal Geology & Exploration,2022,50(3):1−14.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.