Citation: | LI Gensheng, SONG Xianzhi, ZHU Zhaopeng, et al. Research progress and the prospect of intelligent drilling and completion technologies [J]. Petroleum Drilling Techniques,2023, 51(4):35-47. DOI: 10.11911/syztjs.2023040 |
Intelligent drilling and completion technologies are the integration of drilling and completion engineering with Artificial Intelligence (AI), Big Data, cloud computing, and other advanced technologies. They can achieve fine characterization, optimal decision-making, and closed-loop control of oil and gas drilling and completion and are expected to significantly improve drilling and completion efficiency, reservoir drilling rate, and oil and gas recovery efficiency. Therefore, they are the research frontier and hot spot in the oil and gas field. In this paper, the application scenario system of AI in oil and gas drilling and completion was constructed from the engineering practice. Then, the development level of intelligent drilling and completion technologies was divided according to the integration degree of drilling and completion engineering with AI. Furthermore, the research status of intelligent drilling and completion theories and technologies both in China and abroad was discussed, with a medium- and long-term development plan being proposed according to the development trend of AI and drilling and completion engineering. Finally, the problems and key directions of intelligent drilling and completion technologies were summarized. The paper serves as a reference for accelerating the basic theoretical research and application of intelligent drilling and completion technologies in China.
[1] |
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. doi: 10.11698/PED.2021.01.01
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. doi: 10.11698/PED.2021.01.01
|
[2] |
贾承造. 全国油气勘探开发形势与发展前景[J]. 中国石油石化,2022(20):14–17.
JIA Chengzao. National oil and gas exploration and development situation and development prospects[J]. China Petrochem, 2022(20): 14–17.
|
[3] |
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. doi: 10.11911/syztjs.2020001
LI Gensheng, SONG Xianzhi, TIAN Shouceng. Intelligent drilling technology research status and development trends[J]. Petroleum Drilling Techniques, 2020, 48(1): 1–8. doi: 10.11911/syztjs.2020001
|
[4] |
闫铁,许瑞,刘维凯,等. 中国智能化钻井技术研究发展[J]. 东北石油大学学报,2020,44(4):15–21. doi: 10.3969/j.issn.2095-4107.2020.04.003
YAN Tie, XU Rui, LIU Weikai, et al. Research and development of intelligent drilling technology in China[J]. Journal of Northeast Petroleum University, 2020, 44(4): 15–21. doi: 10.3969/j.issn.2095-4107.2020.04.003
|
[5] |
李宗田,肖勇,李宁, 等. 低油价下的页岩油气开发工程技术新进展[J]. 断块油气田,2021,28(5):577–585. doi: 10.6056/dkyqt202105001
LI Zongtian, XIAO Yong, LI Ning, et al. New progress in shale oil and gas development engineering technology under low oil prices [J]. Fault-Block Oil & Gas Field, 2021, 28(5): 577–585. doi: 10.6056/dkyqt202105001
|
[6] |
LI Gensheng, SONG Xianzhi, TIAN Shouceng, et al. Intelligent drilling and completion: a review[J]. Engineering, 2022, 18: 33–48. doi: 10.1016/j.eng.2022.07.014
|
[7] |
杨传书,李昌盛,孙旭东,等. 人工智能钻井技术研究方法及其实践[J]. 石油钻探技术,2021,49(5):7–13. doi: 10.11911/syztjs.2020136
YANG Chuanshu, LI Changsheng, SUN Xudong, et al. Research method and practice of artificial intelligence drilling technology[J]. Petroleum Drilling Techniques, 2021, 49(5): 7–13. doi: 10.11911/syztjs.2020136
|
[8] |
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.
|
[9] |
彭超,邓津辉,谭忠健,等. 基于测井资料的渤中34-9油田火成岩地层抗钻特性评价[J]. 石油钻采工艺,2022,44(2):186–190.
PENG Chao, DENG Jinhui, TAN Zhongjian, et al. Well logging-based anti-drilling property evaluation of igneous rock in Bozhong 34-9 Oilfield[J]. Oil Drilling & Production Technology, 2022, 44(2): 186–190.
|
[10] |
HEGDE C, DAIGLE H, GRAY K E. Performance comparison of algorithms for real-time rate-of-penetration optimization in drilling using data-driven models[J]. SPE Journal, 2018, 23(5): 1706–1722. doi: 10.2118/191141-PA
|
[11] |
孟昭. PDC钻头井下工况评价方法研究[D]. 北京: 中国石油大学(北京), 2020.
MENG Zhao. Study on evaluation method of PDC bit downhole working condition[D]. Beijing: China University of Petroleum (Beijing), 2020.
|
[12] |
REN Chuanjie, HUANG Wenjun, GAO Deli. Predicting rate of penetration of horizontal drilling by combining physical model with machine learning method in the China Jimusar Oil Field[R]. SPE 212294, 2022.
|
[13] |
PACIS F J, ALYAEV S, AMBRUS A, et al. Transfer learning approach to prediction of rate of penetration in drilling[C]//Computational Science–ICCS 2022. Cham: Springer, 2022: 358−371.
|
[14] |
WEI Jun, LIAO Hualin, WANG Huajian, et al. Experimental investigation on the dynamic tensile characteristics of conglomerate based on 3D SHPB system[J]. Journal of Petroleum Science and Engineering, 2022, 213: 110350. doi: 10.1016/j.petrol.2022.110350
|
[15] |
WANG Han, CHEN Dong, YE Zhihui, et al. Intelligent planning of drilling trajectory based on computer vision[R]. SPE 197362, 2019.
|
[16] |
SNYDER J, SALMON G. Intelligent rotary steerable system, coupled with an instrumented bit, delivers section plan in deepwater GOM project[R]. SPE 204680, 2021.
|
[17] |
吴思源,李守定,陈冬,等. 大闭环伺服控制随钻智能导向钻井方法[J]. 地球物理学报,2021,64(11):4215–4226. doi: 10.6038/cjg2021O0449
WU Siyuan, LI Shouding, CHEN Dong, et al. An intelligent-while-drilling steering method of global closed-loop servo control[J]. Chinese Journal of Geophysics, 2021, 64(11): 4215–4226. doi: 10.6038/cjg2021O0449
|
[18] |
田岚. 石油天然气钻井工程风险识别与评价方法[J]. 钻采工艺,2010,33(2):31–33.
TIAN Lan. Risk identification and evaluation method in drilling engineering[J]. Drilling & Production Technology, 2010, 33(2): 31–33.
|
[19] |
NOSHI C I, NOYNAERT S F, SCHUBERT J J. Casing failure data analytics: A novel data mining approach in predicting casing failures for improved drilling performance and production optimization[R]. SPE 191570, 2018.
|
[20] |
AGWU O E, AKPABIO J U, ALABI S B, et al. Artificial intelligence techniques and their applications in drilling fluid engineering: a review[J]. Journal of Petroleum Science and Engineering, 2018, 167: 300–315. doi: 10.1016/j.petrol.2018.04.019
|
[21] |
胜亚楠. 钻井工程风险评估与控制技术研究[D]. 青岛: 中国石油大学(华东), 2019.
SHENG Yanan. Research on risk assessment and control technology of drilling engineering[D]. Qingdao: China University of Petroleum(East China), 2019.
|
[22] |
FANG Chunfei, WANG Zheng, SONG Xianzhi, et al. A novel cementing quality evaluation method based on convolutional neural network[J]. Applied Sciences, 2022, 12(21): 10997. doi: 10.3390/app122110997
|
[23] |
郑双进,程霖,龙震宇,等. 基于GA-SVR算法的顺北区块固井质量预测[J]. 石油钻采工艺,2021,43(4):467–473.
ZHENG Shuangjin, CHENG Lin, LONG Zhenyu, et al. Predicting the cementing quality in Shunbei Block based on GA-SVR algorithm[J]. Oil Drilling & Production Technology, 2021, 43(4): 467–473.
|
[24] |
WUTHERICH K, SRINIVASAN S, RAMSEY L, et al. Engineered diversion: using well heterogeneity as an advantage to designing stage specific diverter strategies[R]. SPE 189827, 2018.
|
[25] |
SOROUSH H, BELYADI H, KANG H, et al. Early prediction and prevention of tip screen-out using deep learning[R]. ARMA-2022 − 0052, 2022.
|
[26] |
GUO Wei, ZHANG Xiaowei, KANG Lixia, et al. Investigation of flowback behaviours in hydraulically fractured shale gas well based on physical driven method[J]. Energies, 2022, 15(1): 325. doi: 10.3390/en15010325
|
[27] |
宋俊强,李晓山,王硕,等. 致密油藏压裂水平井产量预测[J]. 新疆石油地质,2022,43(5):580–586.
SONG Junqiang, LI Xiaoshan, WANG Shuo, et al. Production prediction of fractured horizontal wells in tight oil reservoirs[J]. Xinjiang Petroleum Geology, 2022, 43(5): 580–586.
|
[28] |
于潇伟,和鹏飞,金庭浩. 中国某海油气田智能完井方案设计研究[J]. 石油化工应用,2021,40(9):89–93. doi: 10.3969/j.issn.1673-5285.2021.09.019
YU Xiaowei, HE Pengfei, JIN Tinghao. Study on intelligent well completion scheme design of an offshore oil and gas field in China[J]. Petrochemical Industry Application, 2021, 40(9): 89–93. doi: 10.3969/j.issn.1673-5285.2021.09.019
|
[29] |
舒成龙. 基于井下压力和温度数据的水平井智能完井产液状况分析[D]. 青岛: 中国石油大学(华东), 2015.
SHU Chenglong. Fluid production analysis of intelligent completion horizontal wells based on wellbore pressure and temperature data[D]. Qingdao: China University of Petroleum (East China), 2015.
|
[30] |
SHISHAVAN R A, HUBBELL C, PEREZ H, et al. Combined rate of penetration and pressure regulation for drilling optimization by use of high-speed telemetry[J]. SPE Drilling & Completion, 2015, 30(1): 17–26.
|
[31] |
RITTO T G, SOIZE C, SAMPAIO R. Robust optimization of the rate of penetration of a drill-string using a stochastic nonlinear dynamical model[J]. Computational Mechanics, 2010, 45(5): 415–427. doi: 10.1007/s00466-009-0462-8
|
[32] |
BOURGOYNE A T, Jr, YOUNG F S, Jr. A multiple regression approach to optimal drilling and abnormal pressure detection[J]. SPE Journal, 1974, 14(4): 371–384.
|
[33] |
HEGDE C, SOARES C, GRAY K. Rate of penetration (ROP) modeling using hybrid models: deterministic and machine learning[R]. URTEC-2896522-MS, 2018.
|
[34] |
ETESAMI D, ZHANG W J, HADIAN M. A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks[J]. Journal of Natural Gas Science and Engineering, 2021, 95: 104104. doi: 10.1016/j.jngse.2021.104104
|
[35] |
PAYETTE G S, SPIVEY B J, WANG L, et al. A real-time well-site based surveillance and optimization platform for drilling: Technology, basic workflows and field results[R]. SPE 184615, 2017.
|
[36] |
宋先知,裴志君,王潘涛,等. 基于支持向量机回归的机械钻速智能预测[J]. 新疆石油天然气,2022,18(1):14–20. doi: 10.12388/j.issn.1673-2677.2022.01.002
SONG Xianzhi, PEI Zhijun, WANG Pantao, et al. Intelligent prediction for rate of penetration based on support vector machine regression[J]. Xinjiang Oil & Gas, 2022, 18(1): 14–20. doi: 10.12388/j.issn.1673-2677.2022.01.002
|
[37] |
ZANG Chuanzhen, LU Zongyu, YE Shanlin, et al. Drilling parameters optimization for horizontal wells based on a multiobjective genetic algorithm to improve the rate of penetration and reduce drill string drag[J]. Applied Sciences, 2022, 12(22): 11704. doi: 10.3390/app122211704
|
[38] |
ZHANG Chengkai, SONG Xianzhi, SU Yinao, et al. Real-time prediction of rate of penetration by combining attention-based gated recurrent unit network and fully connected neural networks [J]. Journal of Petroleum Science and Engineering, 2022, 213: 110396. doi: 10.1016/j.petrol.2022.110396
|
[39] |
宋先知, 裴志君, 李根生, 等. 机械钻速预测方法及装置: CN202110786598.1[P]. 2022-02-18.
SONG Xianzhi, PEI Zhijun, LI Gensheng, et al. Mechanical penetration rate prediction method and device: CN202110786598.1[P]. 2022-02-18.
|
[40] |
RASHIDI B, HARELAND G, WU Zebing. Performance, simulation and field application modeling of rollercone bits[J]. Journal of Petroleum Science and Engineering, 2015, 133: 507–517. doi: 10.1016/j.petrol.2015.06.003
|
[41] |
HELMY M W, KHALAF F, DARWISH T A. Well design using a computer model[J]. SPE Drilling & Completion, 1998, 13(1): 42–46.
|
[42] |
ATASHNEZHAD A, WOOD D A, FEREIDOUNPOUR A, et al. Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms[J]. Journal of Natural Gas Science and Engineering, 2014, 21: 1184–1204. doi: 10.1016/j.jngse.2014.05.029
|
[43] |
刘绘新,孟英峰. 定向井最优井身轨迹研究[J]. 天然气工业,2004,24(2):64–67. doi: 10.3321/j.issn:1000-0976.2004.02.019
LIU Huixin, MENG Yingfeng. Study on optimal hole trajectory of directional drilling[J]. Natural Gas Industry, 2004, 24(2): 64–67. doi: 10.3321/j.issn:1000-0976.2004.02.019
|
[44] |
ABBAS A K, ALAMEEDY U, ALSABA M, et al. Wellbore trajectory optimization using rate of penetration and wellbore stability analysis[R]. SPE 193755, 2018.
|
[45] |
BISWAS K, VASANT P M, GAMEZ VINTANED J A, et al. Cellular automata-based multi-objective hybrid grey wolf optimization and particle swarm optimization algorithm for wellbore trajectory optimization[J]. Journal of Natural Gas Science and Engineering, 2021, 85: 103695. doi: 10.1016/j.jngse.2020.103695
|
[46] |
YU Le, PORWAL A, HOLDEN E J, et al. Towards automatic lithological classification from remote sensing data using support vector machines[J]. Computers & Geosciences, 2012, 45: 229–239.
|
[47] |
王延江,杨培杰,史清江,等. 基于支撑向量机的井眼轨迹预测新方法[J]. 石油大学学报(自然科学版),2005,29(5):50–53.
WANG Yanjiang, YANG Peijie, SHI Qingjiang, et al. Novel wellbore trajectory prediction method based on support vector machine[J]. Journal of the University of Petroleum, China(Edition of Natural Science), China, 2005, 29(5): 50–53.
|
[48] |
孟庆华,刘清友. 基于小波–神经网络的井眼轨迹预测数学模型研究[J]. 机械设计,2008,25(9):25–27.
MENG Qinghua, LIU Qingyou. Study on mathematical model of well bore locus prediction based on wavelet-neural network[J]. Journal of Machine Design, 2008, 25(9): 25–27.
|
[49] |
张红,涂忆柳,冯定,等. 基于Kriging代理模型的造斜率预测方法研究[J]. 科学技术与工程,2017,17(3):61–68. doi: 10.3969/j.issn.1671-1815.2017.03.008
ZHANG Hong, TU Yiliu, FENG Ding, et al. Research on prediction method of build-up rate of deflecting tools based on Kriging surrogate model[J]. Science Technology and Engineering, 2017, 17(3): 61–68. doi: 10.3969/j.issn.1671-1815.2017.03.008
|
[50] |
陈冬, 王涵, 叶智慧, 等. 一种基于随钻数据的地质模型重构方法及装置: CN202211215676.3[P]. 2023-01-06.
CHEN Dong, WANG Han, YE Zhihui, et al. A geological model reconstruction method and device based on data while drilling: CN202211215676.3[P]. 2023-01-06.
|
[51] |
刘昊. 一种基于强化学习的井下全闭环智能导钻方法研究[D]. 北京: 中国石油大学(北京), 2020.
LIU Hao. Research on a fully closed-loop intelligent drilling guide d method based on reinforcement learning[D]. Beijing: China University of Petroleum(Beijing), 2020.
|
[52] |
李旭,宋少博,高立军,等. 贝克休斯AutoTrak旋转导向指令成功率与涡轮转数配比研究[J]. 西部探矿工程,2022,34(9):90–91. doi: 10.3969/j.issn.1004-5716.2022.09.032
LI Xu, SONG Shaobo, GAO Lijun, et al. Research on the ratio between the success rate of rotation guidance command and turbine speed of Baker Hughes AutoTrak[J]. West-China Exploration Engineering, 2022, 34(9): 90–91. doi: 10.3969/j.issn.1004-5716.2022.09.032
|
[53] |
BA S, KIM J, GOEL P, et al. Expanding downlink capabilities using autonomous directional drilling with rotary steerable systems[R]. SPE 211067, 2022.
|
[54] |
ABBAS A K, BASHIKH A A, ABBAS H, et al. Intelligent decisions to stop or mitigate lost circulation based on machine learning[J]. Energy, 2019, 183: 1104–1113. doi: 10.1016/j.energy.2019.07.020
|
[55] |
戴永寿,岳炜杰,孙伟峰,等. “三高” 油气井早期溢流在线监测与预警系统[J]. 中国石油大学学报(自然科学版),2015,39(3):188–194.
DAI Yongshou, YUE Weijie, SUN Weifeng, et al. Online monitoring and warning system for early kick foreboding on “three high” wells[J]. Journal of China University of Petroleum(Edition of Natural Science), 2015, 39(3): 188–194.
|
[56] |
SIRUVURI C, NAGARAKANTI S, SAMUEL R. Stuck pipe prediction and avoidance: a convolutional neural network app-roach[R]. SPE 98378, 2006.
|
[57] |
刘建明,李玉梅,张涛,等. 一种基于PCA-RF的卡钻预测方法[J]. 北京信息科技大学学报(自然科学版),2021,36(1):18–22.
LIU Jianming, LI Yumei, ZHANG Tao, et al. Research on PCA-RF-based sticking prediction method[J]. Journal of Beijing Information Science & Technology University, 2021, 36(1): 18–22.
|
[58] |
DUAN Shiming, SONG Xianzhi, CUI Yi, et al. Intelligent kick warning based on drilling activity classification[J]. Geoenergy Science and Engineering, 2023, 222: 211408. doi: 10.1016/j.geoen.2022.211408
|
[59] |
LIANG Haibo, ZOU Jialing, LIANG Wenlong. An early intelligent diagnosis model for drilling overflow based on GA–BP algorithm[J]. Cluster Computing, 2019, 22(5): 10649–10668.
|
[60] |
YIN Qishuai, YANG Jin, TYAGI M, et al. Downhole quantitative evaluation of gas kick during deepwater drilling with deep learning using pilot-scale rig data[J]. Journal of Petroleum Science and Engineering, 2022, 208(Part A): 109136.
|
[61] |
宋先知,姚学喆,李根生,等. 基于LSTM-BP神经网络的地层孔隙压力计算方法[J]. 石油科学通报,2022,7(1):12–23. doi: 10.3969/j.issn.2096-1693.2022.01.002
SONG Xianzhi, YAO Xuezhe, LI Gensheng, et al. A novel method to calculate formation pressure based on the LSTM-BP neural network[J]. Petroleum Science Bulletin, 2022, 7(1): 12–23. doi: 10.3969/j.issn.2096-1693.2022.01.002
|
[62] |
ZHU Zhaopeng, SONG Xianzhi, ZHANG Rui, et al. A hybrid neural network model for predicting bottomhole pressure in managed pressure drilling[J]. Applied Sciences, 2022, 12(13): 6728. doi: 10.3390/app12136728
|
[63] |
许争鸣. 深层高温高压钻井环空气液固三相流动规律研究[D]. 北京: 中国石油大学(北京), 2019.
XU Zhengming. Study on the flowing characteristics of gas-liquid-solid in annulus during high-temperature and high-pressure deep well drilling[D]. Beijing: China University of Petroleum(Beijing), 2019.
|
[64] |
连志龙,周英操,申瑞臣,等. 无意外风险钻井(NDS)技术探讨[J]. 石油钻采工艺,2009,31(1):90–94. doi: 10.3969/j.issn.1000-7393.2009.01.023
LIAN Zhilong, ZHOU Yingcao, SHEN Ruichen, et al. A discussion on technology of no drilling surprises (NDS)[J]. Oil Drilling & Production Technology, 2009, 31(1): 90–94. doi: 10.3969/j.issn.1000-7393.2009.01.023
|
[65] |
杨传书. 钻井风险评价系统DrillRisk的研发与应用[J]. 石油钻探技术,2017,45(5):60–67. doi: 10.11911/syztjs.201705011
YANG Chuanshu. Development and application of risk-assessment system for drilling operations[J]. Petroleum Drilling Techniques, 2017, 45(5): 60–67. doi: 10.11911/syztjs.201705011
|
[66] |
朱玉玺,倪红梅,王瑞仙,等. 人工神经网络在固井质量预测中的应用[J]. 大庆石油学院学报,2002,26(2):52–55.
ZHU Yuxi, NI Hongmei, WANG Ruixian, et al. Application of artificial neutral network to forecasting cementing quality[J]. Journal of Northeast Petroleum University, 2002, 26(2): 52–55.
|
[67] |
倪红梅,王维刚. 免疫神经网络在固井质量预测中的应用研究[J]. 计算机仿真,2009,26(7):267–269. doi: 10.3969/j.issn.1006-9348.2009.07.068
NI Hongmei, WANG Weigang. Application of immune neural network in cementing quality prediction[J]. Computer Simulation, 2009, 26(7): 267–269. doi: 10.3969/j.issn.1006-9348.2009.07.068
|
[68] |
VOLETI D K, REDDICHARLA N, GUNTUPALLI S, et al. Smart way for consistent cement bond evaluation and reducing human bias using machine learning[R]. SPE 202742, 2020.
|
[69] |
SANTOS L, DAHI TALEGHANI A. Machine learning framework to generate synthetic cement evaluation logs for wellbore integrity analysis[R]. ARMA-2021-1769, 2021.
|
[70] |
REOLON D, DI MAGGIO F, MORIGGI S, et al. Unlocking data analytics for the automatic evaluation of cement bond scena-rios[R]. SPWLA-5060, 2020.
|
[71] |
VIGGEN E M, LØVSTAKKEN L, MÅSØY S E, et al. Better automatic interpretation of cement evaluation logs through feature engineering[J]. SPE Journal, 2021, 26(5): 2894–2913. doi: 10.2118/204057-PA
|
[72] |
LEHMAN L V, JACKSON K, NOBLETT B. Big data yields completion optimization: Using drilling data to optimize completion efficiency in a low permeability formation[R]. SPE 181273, 2016.
|
[73] |
SCANLAN W P, PIERSKALLA K J, SOBERNHEIM D W, et al. Optimization of Bakken well completions in a multivariate wor-ld[R]. SPE 189868, 2018.
|
[74] |
KESHAVARZI R, JAHANBAKHSHI R. Investigation of hydraulic and natural fracture interaction: Numerical modeling or artificial intelligence?[R]. ISRM-ICHF-2013 − 025, 2013.
|
[75] |
YANG Ruiyue, QIN Xiaozhou, LIU Wei, et al. A physics-constrained data-driven workflow for predicting coalbed methane well production using artificial neural network[J]. SPE Journal, 2022, 27(3): 1531–1552. doi: 10.2118/205903-PA
|
[76] |
HARPEL J, RAMSEY L, WUTHERICH K. Improving the effectiveness of diverters in hydraulic fracturing of the wolfcamp shale[R]. SPE 191600, 2018.
|
[77] |
HU Jinqiu, KHAN F, ZHANG Laibin, et al. Data-driven early warning model for screenout scenarios in shale gas fracturing operation[J]. Computers & Chemical Engineering, 2020, 143: 107116.
|
[78] |
盛茂, 李雨峰, 李根生, 等. 模型建立方法、裂缝起裂事件诊断方法和装置: CN202110792084.7[P]. 2021-09-24.
SHENG Mao, LI Yufeng, LI Gensheng, et al. Model establishment method, fracture initiation event diagnosis method and device: CN202110792084.7[P]. 2021-09-24.
|
[79] |
DURDYYEV G. New technologies and protocols concerning horizontal well drilling and completion[D]. Turin: Politecnico di Torino, 2021.
|
[80] |
ZHOU Qiumei, DILMORE R, KLEIT A, et al. Evaluating fracture-fluid flowback in Marcellus using data-mining technologies[J]. SPE Production & Operations, 2016, 31(2): 133–146.
|
[81] |
FU Yingkun, DEHGHANPOUR H, EZULIKE D O, et al. Estimating effective fracture pore volume from flowback data and evaluating its relationship to design parameters of multistage-fracture completion[J]. SPE Production & Operations, 2017, 32(4): 423–439.
|
[82] |
MAITY D, CIEZOBKA J. An interpretation of proppant transport within the stimulated rock volume at the hydraulic-fracturing test site in the Permian Basin[J]. SPE Reservoir Evaluation & Engineering, 2019, 22(2): 477–491.
|
[83] |
盛茂,李根生,田守嶒,等. 人工智能在油气压裂增产中的研究现状与展望[J]. 钻采工艺,2022,45(4):1–8.
SHENG Mao, LI Gensheng, TIAN Shouceng, et al. Research status and prospect of artificial intelligence in reservoir fracturing stimulation[J]. Drilling & Production Technology, 2022, 45(4): 1–8.
|
[84] |
NEJAD A M, SHELUDKO S, SHELLEY R F, et al. A case history: evaluating well completions in the Eagle Ford Shale using a data-driven approach[R]. SPE 173336, 2015.
|
[85] |
YANG Ruiyue, LIU Wei, QIN Xiaozhou, et al. A physics-constrained data-driven workflow for predicting coalbed methane well production using a combined gated recurrent unit and multi-layer perception neural network model[R]. SPE 205903, 2021.
|
[86] |
TARIQ Z, ABDULRAHEEM A, KHAN M R, et al. New inflow performance relationship for a horizontal well in a naturally fractured solution gas drive reservoirs using artificial intelligence technique[R]. OTC 28367, 2018.
|
[87] |
BELLO O, YANG D, LAZARUS S, et al. Next generation downhole big data platform for dynamic data-driven well and reservoir management[R]. SPE 186033, 2017.
|
[88] |
WANG Xiaoqiu, WANG Zhiming, ZENG Quanshu. A novel autonomous inflow control device: design, stracture optimization, and fluid sensitivity analysis[R]. IPTC 17758, 2014.
|
[89] |
王小秋,汪志明,赵麟. 基于膨胀材料的新型AICD结构设计及其性能实验研究[J]. 石油科学通报,2018,3(3):302–312.
WANG Xiaoqiu, WANG Zhiming, ZHAO Lin. A novel AICD structure design and its performance analysis[J]. Petroleum Science Bulletin, 2018, 3(3): 302–312.
|
[90] |
陈玉婷, 赵晨晖, 冯超, 等. 深水高产油气田智能完井与防砂一体化技术的应用[J]. 石油工程建设, 2020, 46(增刊1): 229-232.
CHEN Yuting, ZHAO Chenhui, FENG Chao, et al. Integrated application of intelligent well completion and sand control technology for high yield oil and gas fields in deepwater[J]. Petroleum Engineering Construction, 2020, 46(supplement 1): 229-232.
|
[91] |
HEGDE C, MILLWATER H, PYRCZ M, et al. Rate of penetration (ROP) optimization in drilling with vibration control[J]. Journal of Natural Gas Science and Engineering, 2019, 67: 71–81. doi: 10.1016/j.jngse.2019.04.017
|
[92] |
LOSOYA E Z, GILDIN E, NOYNAERT S F, et al. An open-source enabled drilling simulation consortium for academic and commercial applications[R]. SPE 198943, 2020.
|
[93] |
宋先知, 裴志君, 王潘涛, 等. 基于多目标的油气钻井策略预测方法及装置: CN202110232999.2[P]. 2021-05-14.
SONG Xianzhi, PEI Zhijun, WANG Pantao, et al. Prediction method and device of oil and gas drilling strategy based on multi-objective: CN202110232999.2[P]. 2021-05-14.
|
[94] |
徐宝昌, 张学智. 耦合井筒与钻柱的钻井过程全局动态建模与仿真[C]//第40届中国控制会议论文集(15). 上海: 中国自动化学会控制理论专业委员会, 2021: 688 − 693.
XU Baochang, ZHANG Xuezhi. Global dynamic modeling and simulation of drilling process coupled with wellbore and drill string[C]//Proceedings of the 40th China control conference (15). Shanghai: Control theory professional committee of China automation society, 2021: 688−693.
|
[95] |
MAYANI M G, ROMMETVEIT R, OEDEGAARD S I, et al. Drilling automated realtime monitoring using digital twin[R]. SPE 192807, 2018.
|
[96] |
CAYEUX E. Mathematical modelling of the drilling process for real-time applications in drilling simulation, interpretation and assistance[D]. Stavanger: University of Stavanger, 2019.
|
[97] |
肖立志. 机器学习数据驱动与机理模型融合及可解释性问题[J]. 石油物探,2022,61(2):205–212. doi: 10.3969/j.issn.1000-1441.2022.02.002
XIAO Lizhi. The fusion of data-driven machine learning with mechanism models and interpretability issues[J]. Geophysical Prospecting for Petroleum, 2022, 61(2): 205–212. doi: 10.3969/j.issn.1000-1441.2022.02.002
|
[98] |
RAI R, SAHU C K. Driven by data or derived through physics? A review of hybrid physics guided machine learning techniques with cyber-physical system (CPS) focus[J]. IEEE Access, 2020, 8: 71050–71073. doi: 10.1109/ACCESS.2020.2987324
|
[99] |
杨顺辉,郭珍珍,张洪宝,等. 基于集成迁移学习的机械钻速预测[J]. 计算机系统应用,2022,31(10):270–278.
YANG Shunhui, GUO Zhenzhen, ZHANG Hongbao, et al. Rate of penetration prediction using ensemble transfer learning[J]. Computer Systems & Applications, 2022, 31(10): 270–278.
|
[100] |
CHEN Yuntian, ZHANG Dongxiao. Physics-constrained deep learning of geomechanical logs[J]. IEEE Transactions on Geosci-ence and Remote Sensing, 2020, 58(8): 5932–5943. doi: 10.1109/TGRS.2020.2973171
|
[101] |
陈良臣,傅德印. 面向小样本数据的机器学习方法研究综述[J]. 计算机工程,2022,48(11):1–13.
CHEN Liangchen, FU Deyin. Survey on machine learning methods for small sample data[J]. Computer Engineering, 2022, 48(11): 1–13.
|
[102] |
GILPIN L H, BAU D, YUAN B Z, et al. Explaining explanations: an overview of interpretability of machine learning[C]//2018 IEEE 5th international conference on data science and advanced analytics (DSAA). Turin: IEEE, 2018: 80−89.
|
[103] |
CHOI E, BAHADORI M T, SCHUETZ A, et al. RETAIN: Interpretable predictive model in healthcare using reverse time attention mechanism[EB/OL]. (2016-08-19)[2023-01-15]. https://arxiv. org/abs/1608.05745v1.
|
[104] |
BARR KUMARAKULASINGHE N, BLOMBERG T, LIU Jintai, et al. Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models[C]//2020 IEEE 33rd international symposium on computer-based medical systems (CBMS). Rochester: IEEE, 2020: 7-12.
|
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刘文堂,刘昱彤. 温度对油基钻井液黏度测定的影响因素分析. 石油石化节能与计量. 2025(01): 16-20 .
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贾永红,周双君,段利波,逄凯迪,温杰,陈琳波. 抗180℃水基钻井液随钻堵漏剂的研制及性能评价. 化工设计通讯. 2025(01): 16-19 .
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邓文彪,韩成,李文拓,魏佳,郭宇堃. 海上抗高温高密度油基钻井液技术及应用. 化学工程与装备. 2024(02): 33-36 .
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许林,王晓棠,王晓亮,胡南琪,许明标,韩银府,位中伟,丁梓敬. 超支化高分子水基钻井液仿生润滑机理. 天然气工业. 2024(07): 120-131 .
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邓正强,欧猛,许桂莉,黄坤,梁睿,黄平,罗宇峰,胡嘉. 页岩气窄密度窗口地层封堵承压油基钻井液技术. 石油化工应用. 2023(03): 53-57 .
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于雷,李公让,王宝田,张高峰,张守文,明玉广. 一种新型亲油纤维堵漏剂的研发. 天然气工业. 2023(06): 112-118 .
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陈建宏,汤柏松,罗伟,杜雪雷,方牧. 渤海西部海域某区块断层防漏、堵漏技术研究及应用. 天津科技. 2023(09): 28-30 .
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董云峰,韩成. 油基钻井液堵漏体系及材料研究进展. 化工设计通讯. 2023(11): 37-39 .
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王均,罗陶涛,蒲克勇,陶操. 适于涪陵页岩气田储集层的油基钻井液承压堵漏材料. 材料导报. 2022(06): 124-128 .
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孙凯,刘化伟,明鑫,乐守群. 自201井区页岩气井水平段安全高效钻井技术. 钻探工程. 2022(02): 104-109 .
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![]() | |
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陈亮,胡进科,耿冬,李子钰. 重庆页岩气井油基钻井液堵漏防塌新工艺探索. 油气藏评价与开发. 2021(04): 527-535 .
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陈军,陈小龙. 低成本延迟交联凝胶堵漏体系研究. 山东化工. 2020(06): 141-144+147 .
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顾雪凡,王棚,高龙,陈刚,张洁. 我国天然高分子基钻井液体系研究进展. 西安石油大学学报(自然科学版). 2020(05): 83-91 .
![]() | |
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刘政,李俊材,黄鸿. 准噶尔南缘油基膨胀型随钻防漏堵漏技术. 新疆石油天然气. 2020(02): 43-47+3 .
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郝海洋,屈勇,何吉标,张家瑞,刘俊君. 页岩气水平井低密度防窜水泥浆增稠机理. 天然气勘探与开发. 2020(04): 131-137 .
![]() | |
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代一钦. 油基钻井液条件下堵漏材料研究新进展. 江汉石油职工大学学报. 2020(06): 57-59 .
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张杜杰,金军斌,陈瑜,康毅力. 深部裂缝性致密储层随钻堵漏材料补充时机研究. 特种油气藏. 2020(06): 158-164 .
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袁青松,冯辉,张栋,李中明,代磊,董果果. 强封堵钻井液体系在河南页岩气钻井中的研究和应用. 钻井液与完井液. 2019(01): 29-35 .
![]() | |
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马文英,刘昱彤,钟灵,刘文堂,孙东营,毛世发. 油基钻井液封堵剂研究及应用. 断块油气田. 2019(04): 529-532 .
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宋保健,孙凯,乐守群,兰凯,明鑫. 涪陵页岩气田钻井提速难点与对策分析. 钻采工艺. 2019(04): 9-12+6 .
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刘彦学. 松南气田低密度低伤害随钻堵漏钻井液技术. 钻井液与完井液. 2019(04): 442-448 .
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曾德智,喻智明,何奇垚,刘乔平,施太和. 页岩气井环空带压安全风险定量评价方法研究. 西南石油大学学报(自然科学版). 2019(06): 146-154 .
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王武斌. JY68-2井复杂情况钻井液预防与处理技术对策. 化工管理. 2018(08): 242 .
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匡立新,刘卫东,甘新星,姜政华,陈士奎. 涪陵平桥南区块页岩气水平井钻井提速潜力分析. 石油钻探技术. 2018(04): 16-22 .
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梁文利. 深层页岩气油基钻井液承压堵漏技术. 钻井液与完井液. 2018(03): 37-41 .
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吴江,李龙,任冠龙,张崇. 海上复杂易垮塌地层高性能油基钻井液研发与应用. 钻井液与完井液. 2018(05): 55-60 .
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