Research Progress and Development Prospect of Intelligent Surface Logging Technology

WANG Zhizhan

doi:10.11911/syztjs.2024099

Volume 52 Issue 5

Nov. 2024

Turn off MathJax

Article Contents

Abstract

References

Petroleum Drilling Techniques > 2024 > 52(5): 51-61. > DOI: 10.11911/syztjs.2024099

WANG Zhizhan. Research progress and development prospect of intelligent surface logging technology [J]. Petroleum Drilling Techniques, 2024, 52(5)：51−61. DOI: 10.11911/syztjs.2024099

Citation:

WANG Zhizhan. Research progress and development prospect of intelligent surface logging technology [J]. Petroleum Drilling Techniques, 2024, 52(5)：51−61. DOI: 10.11911/syztjs.2024099

Citation:

WANG Zhizhan. Research progress and development prospect of intelligent surface logging technology [J]. Petroleum Drilling Techniques, 2024, 52(5)：51−61. DOI: 10.11911/syztjs.2024099

PDF (2059 KB)

Research Progress and Development Prospect of Intelligent Surface Logging Technology

WANG Zhizhan^{1, 2,}

1.
Sinopec Key Laboratory of Well Logging, Beijing, 102206, China
2.
Sinopec Research Institute of Petroleum Engineering Co., Ltd., Beijing, 102206, China

More Information

Received Date: May 07, 2024
Available Online: October 08, 2024

Graphical Abstract

Abstract

Abstract

Surface logging has the characteristics of complex sample conditions and sample preparation process, numerous and discrete collection items, strong dependence on hands-on experience, and low per capita output. It is urgent to strengthen intelligentialization transformation. However, compared with other petroleum engineering technologies, intelligent surface logging is making slow advancement and facing application limits. Therefore, the progress and gap of hardware systems, control systems, and application systems of intelligent drilling in China and abroad were analyzed from the progress and achievement of intelligent drilling. Then, the main technical progress of intelligent surface logging was analyzed in terms of geological surface logging, engineering surface logging, and intelligent platform, covering “data +” driven and visually driven lithology identification, fluid identification, and downhole and surface risk identification and early alarming. Based on the comparison between intelligent drilling and intelligent surface logging, it was suggested that the research and development of hardware systems such as downhole intelligent surface logging, and intelligent surface logging robots, as well as software systems such as multi-field digital twins, multi-acquisition intelligent control, multimode large model of surface logging, and intelligent interpretation and evaluation should be strengthened. At the same time, it was emphasized that we should attach great importance to and rationally look at the development of intelligent surface logging and determine strategic positioning and process optimization on the basis of retrospective evaluation and horizontal comparison, so as to catch up with the progress and engagement. These analyses and viewpoints have guiding significance in promoting the benign and rapid development of intelligent surface logging.
- Intelligent surface logging,
- intelligent drilling,
- robots,
- digital twin,
- large model,
- intelligent control

FullText(HTML)

References (79)

References

[1]	李根生,宋先知,祝兆鹏,等. 智能钻完井技术研究进展与前景展望[J]. 石油钻探技术,2023,51(4):35–47. doi: 10.11911/syztjs.2023040 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
[2]	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. doi: 10.7623/syxb202004013 WANG Minsheng, GUANG Xinjun. Status and development trends of intelligent drilling technology[J]. Acta Petrolei Sinica, 2020, 41(4): 505–512. doi: 10.7623/syxb202004013
[3]	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. 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.
[4]	MCCARTHY J, MINSKY M L, ROCHESTER N, et al. A proposal for the Dartmouth summer research project on artificial intelligence: August 31, 1955[J]. AI Magazine, 2006, 27(4): 12–14.
[5]	GAINITDINOV B, MESHALKIN Y, ORLOV D, et al. Predicting mineralogical composition in unconventional formations using machine learning and well logging data[R]. IPTC 23487, 2024.
[6]	YANG Tao, ARIEF I H, NIEMANN M, et al. A machine learning approach to predict gas oil ratio based on advanced mud gas data[R]. SPE 195459, 2019.
[7]	陈凯枫,杨学文,宋先知,等. 基于工程录井数据的井漏智能诊断方法[J]. 石油机械,2022,50(11):16–22. CHEN Kaifeng, YANG Xuewen, SONG Xianzhi, et al. An intelligent diagnosis method for lost circulation based on engineering logging data[J]. China Petroleum Machinery, 2022, 50(11): 16–22.
[8]	刘枫. 顺北地区地层四压力智能预测软件研发与应用[D]. 北京:中国石油大学(北京),2023. LIU Feng. Research and application of intelligent prediction software for formation four pressure in Shunbei Area[D]. Beijing: China University of Petroleum(Beijing), 2023.
[9]	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
[10]	王志战. 一体化、智能化时代的录井技术发展方向探讨[J]. 录井工程,2020,31(1):1–6. doi: 10.3969/j.issn.1672-9803.2020.01.001 WANG Zhizhan. Discussion on the development direction of mud logging technology in the era of integration and intellectualization[J]. Mud Logging Engineering, 2020, 31(1): 1–6. doi: 10.3969/j.issn.1672-9803.2020.01.001
[11]	王志战. 中国石化录井技术新进展与发展方向思考[J]. 石油钻探技术,2023,51(4):124–133. doi: 10.11911/syztjs.2023027 WANG Zhizhan. Thoughts for new progress and development directions of Sinopec's surface logging technology[J]. Petroleum Drilling Techniques, 2023, 51(4): 124–133. doi: 10.11911/syztjs.2023027
[12]	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
[13]	张鑫鑫,梁博文,张晓龙,等. 智能钻井装备与技术研究进展[J]. 煤田地质与勘探,2023,51(9):20–30. doi: 10.12363/issn.1001-1986.23.06.0324 ZHANG Xinxin, LIANG Bowen, ZHANG Xiaolong, et al. Research progress of intelligent drilling equipment and technology[J]. Coal Geology & Exploration, 2023, 51(9): 20–30. doi: 10.12363/issn.1001-1986.23.06.0324
[14]	HU Qin, LIU Qingyou. Intelligent drilling: a prospective technology of tomorrow[R]. SPE 103781, 2006.
[15]	RASSENFOSS S. Drilling automation: a robot takes over the drilling floor[J]. Journal of Petroleum Technology, 2021, 73(12): 18–22. doi: 10.2118/1221-0018-JPT
[16]	LAWRENCE L, REDMOND B, RUSSELL R B, et al. Intelligent wired drill-pipe system provides significant improvements in drilling performance on offshore Australia development[R]. OTC 20067, 2009.
[17]	JELLISON M J, PRIDECO G, HALL D R. Intelligent drill pipe creates the drilling network[R]. SPE 80454, 2003.
[18]	TURNER D R, HEAD P F, YURATICH M A, et al. The all electric BHA: recent developments toward an intelligent coiled-tubing drilling system[R]. SPE 54469, 1999.
[19]	VALVERDE E. Intelligent near-bit reamer affords same-trip drilling, hole enlargement and rathole reduction for optimal deepwater well construction[R]. OTC 28402, 2018.
[20]	TILLEY J, NAIR V N, HAMOUDI L. Case study: intelligent RSS improving drilling performance on three mile laterals in the Appalachian Basin[R]. SPE 201723, 2020.
[21]	AL ARFI S, ALSOWAIDI F, RUIZ F, et al. New intelligent push-the-bit rotary steerable system helped reducing well time and maximized directional drilling performance, Abu Dhabi, UAE[R]. SPE 207537, 2021.
[22]	CORSER G P, HARMSE J E, CORSER B A, et al. Field test results for a real-time intelligent drilling monitor[R]. SPE 59227, 2000.
[23]	ZHU J, ZENG L. Intelligent pressure control system on drilling process[R]. OTC 30828, 2020.
[24]	SHEN Xinyu, LIU Sen, SU Qiang, et al. Intelligent switch control algorithm of the push-the-bit rotary steerable drilling system[R]. ARMA 20233-0558, 2023.
[25]	ROWSELL P J, WALLER M D. Intelligent control of drilling systems[R]. SPE 21927, 1991.
[26]	WAN Youwei, LIU Xiangjun, XIONG Jian, et al. Intelligent prediction of drilling rate of penetration based on method-data dual validity analysis[J]. SPE Journal, 2024, 29(5): 2257–2274. doi: 10.2118/217977-PA
[27]	ABUGHABAN M, ALSHAARAWI A, MENG Cui, et al. Optimization of drilling performance based on an intelligent drilling advisory system[R]. IPTC 19269, 2019.
[28]	XIE Tao, HOU Xinxin, HUO Hongbo, et al. Improving drilling efficiency using intelligent decision system for drilling in Bohai Oilfield based on big data[R]. SPE 215427, 2023.
[29]	RASHIDI B, HARELAND G, TAHMEEN M, et al. Real-time bit wear optimization using the intelligent drilling advisory system[R]. SPE 136006, 2010.
[30]	WANG Jianhua, GUAN Zhen, LIU Muchen, et al. Drilling stuck probability intelligent prediction based on LSTM considering local interpretability[R]. ARMA 2023-0326, 2023.
[31]	殷启帅,杨进,曹博涵,等. 基于长短期记忆神经网络的深水钻井工况实时智能判别模型[J]. 石油钻采工艺,2022,44(1):97–104. YIN Qishuai, YANG Jin, CAO Bohan, et al. Real-time intelligent rig activities classification model of deep-water drilling using long short-term memory (LSTM) network[J]. Oil Drilling & Production Technology, 2022, 44(1): 97–104.
[32]	李雪松,张骁,管震,等. 基于图像识别技术的钻井井漏溢流智能报警系统开发[J]. 世界石油工业,2021,28(1):48–54. LI Xuesong, ZHANG Xiao, GUAN Zhen, et al. Development of the drilling mud loss and overflow intelligent alarm system based on the image recognition technology[J]. World Petroleum Industry, 2021, 28(1): 48–54.
[33]	WANG Han, CHEN Dong, YE Zhihui, et al. Intelligent planning of drilling trajectory based on computer vision[R]. SPE 197362, 2019.
[34]	张晓东,朱正喜. 智能钻井技术研究[J]. 石油钻采工艺,2010,32(1):1–4. doi: 10.3969/j.issn.1000-7393.2010.01.002 ZHANG Xiaodong, ZHU Zhengxi. Study of intelligent drilling technology[J]. Oil Drilling & Production Technology, 2010, 32(1): 1–4. doi: 10.3969/j.issn.1000-7393.2010.01.002
[35]	de WARDT J. Guest editorial: trends in remote operations and drilling systems automation point to an expanding footprint what comes next and when?[J]. Journal of Petroleum Technology, 2022, 74(11): 10–13. doi: 10.2118/1122-0010-JPT
[36]	刘合. 油气勘探开发数字化转型人工智能应用大势所趋[J]. 石油科技论坛,2023,42(3):1–9. LIU He. Digital transformation of oil and gas exploration and development; unstoppable AI application[J]. Petroleum Science and Technology Forum, 2023, 42(3): 1–9.
[37]	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
[38]	ROWE H, MAINALI P, NIETO M, et al. Geochemical perspectives on cuttings-based chemostratigraphy and mineral modeling in the Delaware Basin, Texas and New Mexico[R]. URTEC 2019-1068, 2019.
[39]	HUSSAIN M, AMAO A, AL-RAMADAN K, et al. A novel method to develop chemostratigraphy using X-ray fluorescence spectral raw data[R]. URTEC 2021-5478, 2021.
[40]	MICHAEL N A, SCHEIBE C, CRAIGIE N W. Automations in chemostratigraphy: toward robust chemical data analysis and interpretation[R]. SPE 204892, 2021.
[41]	唐诚,王崇敬,梁波,等. 基于机器学习算法的页岩气评价参数计算模型研究[J]. 录井工程,2021,32(4):18–22. doi: 10.3969/j.issn.1672-9803.2021.04.003 TANG Cheng, WANG Chongjing, LIANG Bo, et al. Study on shale gas evaluation parameter calculation model based on machine learning algorithm[J]. Mud Logging Engineering, 2021, 32(4): 18–22. doi: 10.3969/j.issn.1672-9803.2021.04.003
[42]	刘雨龙. 基于深度学习的岩屑智能分析方法研究[D]. 北京:中国石油大学(北京),2023. LIU Yulong. Research on intelligent analysis method of cuttings based on deep learning[D]. Beijing: China University of Petro-leum(Beijing), 2023.
[43]	夏文鹤,谢万洋,唐印东,等. 砂样岩屑图像特征的岩性智能高效识别[J]. 石油地球物理勘探,2023,58(3):495–506. XIA Wenhe, XIE Wanyang, TANG Yindong, et al. Intelligent and efficient lithology identification based on image features of returned cuttings[J]. Oil Geophysical Prospecting, 2023, 58(3): 495–506.
[44]	张德君,魏伟,刘明艳,等. 基于综合录井数据的地层岩性智能识别方法[J]. 西部探矿工程,2023,35(4):54–59. doi: 10.3969/j.issn.1004-5716.2023.04.016 ZHANG Dejun, WEI Wei, LIU Mingyan, et al. Intelligent identification method of formation lithology based on comprehensive logging data[J]. West-China Exploration Engineering, 2023, 35(4): 54–59. doi: 10.3969/j.issn.1004-5716.2023.04.016
[45]	JACOBS T. Mud-gas breakthrough Equinor develops real-time reservoir-fluid identification[J]. Journal of Petroleum Technolog, 2021, 73(2): 37–39. doi: 10.2118/0221-0037-JPT
[46]	HAFIDZ ARIEF I, YANG Tao. Real time reservoir fluid log from advanced mud gas data[R]. SPE 201323, 2020.
[47]	YANG Tao, YERKINKYZY G, ULEBERG K, et al. Predicting reservoir fluid properties from advanced mud gas data[J]. SPE Reservoir Evaluation & Engineering, 2021, 24(2): 358–366.
[48]	UNGAR F, MCGILL A, NYGAARD M T, et al. Fluid identification from mud gas in the overburden: a case study for the Snorre Field[R]. SPE 214440, 2023.
[49]	YANG Tao, ULEBERG K, CELY A, et al. Unlock large potentials of standard mud gas for real-time fluid typing[R]. SPWLA 2022-0007, 2022.
[50]	KOPAL M, YERKINKYZY G, NYGÅRD M T, et al. Real-time fluid identification from integrating advanced mud gas and petrophysical logs[R]. SPWLA 2022-0009, 2022.
[51]	BUCKLE P S G, ABDULLAH A F H, ZAINI N, et al. Utilization of digitalized numerical model derived from advanced mud gas data for low cost fluid phase identification, derisking drilling and effective completion plan in depleted reservoir[R]. SPWLA 2022-0092, 2022.
[52]	WRIGHT A C. Estimation of gas/oil ratios and detection of unusual formation fluids from mud logging gas data[R]. SPWLA 1996-CC, 1996.
[53]	MALIK M, HANSON S A, CLINCH S. Maximizing value from mudlogs: integrated approach to determine net pay[R]. SPWLA 5028, 2020.
[54]	严伟丽,高楚桥,赵彬,等. 基于气测录井资料的气油比定量计算方法[J]. 科学技术与工程,2020,20(23):9287–9292. YAN Weili, GAO Chuqiao, ZHAO Bin, et al. Quantitative calculation method of gas-oil ratio in gas logging data[J]. Science Technology and Engineering, 2020, 20(23): 9287–9292.
[55]	JIANG Han, DAIGLE H, TIAN Xiao, et al. A comparison of clustering algorithms applied to fluid characterization using NMRT₁-T₂ maps of shale[J]. Computers & Geosciences, 2019, 126: 52–61.
[56]	夏文鹤,唐印东,李皋,等. 基于岩屑录井图像的井壁稳定性智能预测方法[J]. 天然气工业,2023,43(12):71–83. XIA Wenhe, TANG Yindong, LI Gao, et al. An intelligent prediction method for wellbore stability based on drilling cuttings logging images[J]. Natural Gas Industry, 2023, 43(12): 71–83.
[57]	ZHANG Shaohui, HUANG Weihe, BI Guoqiang, et al. Intelligent risk identification and warning model for typical drilling operation scenes and its application[R]. SPE 214599, 2023.
[58]	胡志强,杨进,王磊,等. 钻井工况智能识别与时效分析技术[J]. 石油钻采工艺,2022,44(2):241–246. HU Zhiqiang, YANG Jin, WANG Lei, et al. Intelligent identification and time-efficiency analysis of drilling operation conditions[J]. Oil Drilling & Production Technology, 2022, 44(2): 241–246.
[59]	张矿生,宫臣兴,陆红军,等. 基于集成学习的井漏智能预警模型及智能推理方法[J]. 石油钻采工艺,2023,45(1):47–54. ZHANG Kuangsheng, GONG Chenxing, LU Hongjun, et al. Intelligent early warning model and intelligent reasoning method based on integrated learning for loss circulation[J]. Oil Drilling & Production Technology, 2023, 45(1): 47–54.
[60]	张敏. 渤海中深层钻井地层三压力智能预测方法研究[D]. 北京:中国石油大学(北京),2023. ZHANG Min. Research on the intelligent prediction method of triple pressure in the middle and deep drilling strata in Bohai Sea[D]. Beijing: China University of Petroleum(Beijing), 2023.
[61]	阎荣辉,黄子舰,杨永强,等. “互联网+” 时代的智慧录井系统应用探索[J]. 录井工程,2020,31(2):1–5. doi: 10.3969/j.issn.1672-9803.2020.02.001 YAN Ronghui, HUANG Zijian, YANG Yongqiang, et al. Application and exploration of smart logging system in the Internet Plus era[J]. Mud Logging Engineering, 2020, 31(2): 1–5. doi: 10.3969/j.issn.1672-9803.2020.02.001
[62]	方铁园,马宏伟,郭龙飞,等. 智慧录井平台建设及其在长庆油田的创新应用[J]. 录井工程,2023,34(3):97–101. doi: 10.3969/j.issn.1672-9803.2023.03.015 FANG Tieyuan, MA Hongwei, GUO Longfei, et al. Construction of smart mud logging platform and its innovative application in Changqing Oilfield[J]. Mud Logging Engineering, 2023, 34(3): 97–101. doi: 10.3969/j.issn.1672-9803.2023.03.015
[63]	张锦宏,周爱照,成海,等. 中国石化石油工程技术新进展与展望[J]. 石油钻探技术,2023,51(4):149–158. ZHANG Jinhong, ZHOU Aizhao, CHENG Hai, et al. New progress and prospects for Sinopec’s petroleum engineering technologies[J]. Petroleum Drilling Techniques, 2023, 51(4): 149–158.
[64]	梁海波,宋洋,于志刚,等. 钻井液流变性实时测量方法及系统研究[J]. 石油机械,2022,50(1):10–18. LIANG Haibo, SONG Yang, YU Zhigang, et al. Real-time measurement method and systematic study on drilling fluid rheology[J]. China Petroleum Machinery, 2022, 50(1): 10–18.
[65]	孟济良,吴龙斌,张学亭. 岩屑录井的新技术:DL-1型地质录井自动捞砂机介绍[J]. 石油勘探与开发,1984,11(5):75–79. MENG Jiliang, WU Longbin, ZHANG Xueting. A new technology of cuttings logging: DL-1 type automatic cuttings acquisition machine[J]. Petroleum Exploration and Development, 1984, 11(5): 75–79.
[66]	ALSHEHRI A, KATTERBAUER K, YOUSEF A. Real-time autoregressive deep learning framework for in-line automatic surface logging[R]. SPE 214079, 2023.
[67]	LI Sanguo, XIAO Lizhi, LI Xin, et al. A novel NMR instrument for real time drilling fluid analysis[J]. Microporous and Mesoporous Materials, 2018, 269: 138–141. doi: 10.1016/j.micromeso.2017.08.038
[68]	张志财,刘保双,王忠杰,等. 钻井液性能在线监测系统的研制与现场应用[J]. 钻井液与完井液,2020,37(5):597–601. ZHANG Zhicai, LIU Baoshuang, WANG Zhongjie, et al. Development and field application of an online drilling fluid property monitoring system[J]. Drilling Fluid & Completion Fluid, 2020, 37(5): 597–601.
[69]	王鹏,刘伟,张果. 钻井液性能自动监测装置的现状及改进建议[J]. 钻采工艺,2022,45(3):42–47. doi: 10.3969/J.ISSN.1006-768X.2022.03.08 WANG Peng, LIU Wei, ZHANG Guo. Status quo and improvement suggestions of automatic monitoring equipment for drilling fluid performance[J]. Drilling & Production Technology, 2022, 45(3): 42–47. doi: 10.3969/J.ISSN.1006-768X.2022.03.08
[70]	张好林,杨传书,李昌盛,等. 钻井数字孪生系统设计与研发实践[J]. 石油钻探技术,2023,51(3):58–65. ZHANG Haolin, YANG Chuanshu, LI Changsheng, et al. Design and research practice of a drilling digital twin system[J]. Petroleum Drilling Techniques, 2023, 51(3): 58–65.
[71]	BALACHANDRAN P A, PADMANABHAN K V K. Integrated operations system: implementation of a truly integrated digital oil field and development of digital twin[R]. OTC 32841, 2023.
[72]	WANG Peixian, WANG Xiaolin, MA Jun, et al. Digital and intelligent technology in underground gas storage operation based on digital twin technologies[R]. ISOPE I-23-008, 2023.
[73]	BUSOLLO C, ABDO E, KATTAR M, et al. Evolution of a digital twin for underground gas storage wells: thermal effects of tubing gas flow on annulus pressure in transient conditions[R]. SPE 220006, 2024.
[74]	WANG Peixian, MA Jun, LI Zunzhao, et al. Integrated simulation technology of underground gas storage based on digital twin technologies[R]. ISOPE I-24-043, 2024.
[75]	刘合,任义丽,李欣,等. 油气行业人工智能大模型应用研究现状及展望[J]. 石油勘探与开发,2024,51(4):910–923. LIU He, REN Yili, LI Xin, et al. Research status and application of artificial intelligence large models in the oil and gas industry[J]. Petroleum Exploration and Development, 2024, 51(4): 910–923.
[76]	YI M, CEGLINSKI K, ASHOK P, et al. Applications of large language models in well construction planning and real-time operation[R]. SPE 217700, 2024.
[77]	PACIS F J, ALYAEV S, PELFRENE G, et al. Enhancing information retrieval in the drilling domain: zero-shot learning with large language models for question-answering[R]. SPE 217671, 2024.
[78]	AMEUR-ZAIMECHE O, KECHICHED R, HEDDAM S, et al. Real-time porosity prediction using gas-while-drilling data and machine learning with reservoir associated gas: case study for Hassi Messaoud Field, Algeria[J]. Marine and Petroleum Geology, 2022, 140: 105631. doi: 10.1016/j.marpetgeo.2022.105631
[79]	OULADMANSOUR A, AMEUR-ZAIMECHE O, KECHICHED R, et al. Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone reservoirs. Case study: Rhourd Chegga Oilfield, Algeria[J]. Geoenergy Science and Engineering, 2023, 223: 211511.

[1]	YIN Shuai, ZHAO Junhui, LIU Ping, SHEN Zhicheng. Opening Conditions and Extension Law of Natural and Hydraulic Fractures in Fractured Reservoirs[J]. Petroleum Drilling Techniques, 2024, 52(3): 98-105. DOI: 10.11911/syztjs.2024022
[2]	SHU Honglin, LIU Chen, LI Zhiqiang, DUAN Guifu, LAI Jianlin, JIANG Ming. Numerical Simulation of Complex Fracture Propagation in Shallow Shale Gas Fracturing in Zhaotong[J]. Petroleum Drilling Techniques, 2023, 51(6): 77-84. DOI: 10.11911/syztjs.2023095
[3]	ZHAO Huan, LI Wei, TANG Pengfei, WANG Xiao, ZHANG Minghui, WANG Jianbo. Study on the Distribution Law of Near-Wellbore in-situ Stress and Casing Load under Fracturing Conditions[J]. Petroleum Drilling Techniques, 2023, 51(5): 106-111. DOI: 10.11911/syztjs.2023092
[4]	ZHANG Yiqun, HU Xiao, WU Xiaoya, LI Gensheng, TIAN Shouceng, ZHAO Shuai. Experimental and Numerical Simulation Study of Natural Gas Hydrate Erosion by Swirling Jet[J]. Petroleum Drilling Techniques, 2022, 50(3): 24-33. DOI: 10.11911/syztjs.2022046
[5]	WANG Yi. A Method for Accurate Calculation of Pore Pressure in Fractured Formations of Shale Gas Reservoirs[J]. Petroleum Drilling Techniques, 2020, 48(3): 29-34. DOI: 10.11911/syztjs.2020056
[6]	LI Wei, ZHAO Huan, LI Siqi, Ll Li, SUN Wenfeng. 2D Characterization of Geometric Features and Connectivity of Fracture Networks in Shale Formations[J]. Petroleum Drilling Techniques, 2017, 45(6): 70-76. DOI: 10.11911/syztjs.201706013
[7]	YANG Yingtao, WEN Qingzhi, DUAN Xiaofei, WANG Shuting, WANG Feng. Numerical Simulation for Flow Conductivity in Channeling Fractures[J]. Petroleum Drilling Techniques, 2016, 44(6): 104-110. DOI: 10.11911/syztjs.201606018
[8]	LI Yumei, LYU Wei, SONG Jie, LI Jun, YANG Hongwei, YU Liwei. Numerical Simulation Study on the Complex Network Fractures of Stratified Shale Gas Reservoirs[J]. Petroleum Drilling Techniques, 2016, 44(4): 108-113. DOI: 10.11911/syztjs.201604019
[9]	PENG Hao, LI Qian, YIN Hu, TANG Zhiqiang. A New Solution Method for the Lietard Natural Fracture Width Prediction Model[J]. Petroleum Drilling Techniques, 2016, 44(3): 72-76. DOI: 10.11911/syztjs.201603013
[10]	Liu Yu, Ai Chi. Opening of Natural Fractures under Induced Stress in Multi-Stage Fracturing[J]. Petroleum Drilling Techniques, 2015, 43(1): 20-26. DOI: 10.11911/syztjs.201501004

Cited By

Cited by

Periodical cited type(4)

1.	陈德春，张文宣，阳成，常峰，邴绍强，张鹏，王亮亮，马硕. 基于井口流体温度的含水率计算模型. 断块油气田. 2025(03): 514-521 .
2.	魏锋，陈现，王迪，夏瑜. 海上少井条件下含水率计算方法研究及应用. 海洋石油. 2022(04): 63-66 .
3.	王谦，谭茂金，石玉江，李高仁，程相志，罗伟平. 径向基函数神经网络法致密砂岩储层相对渗透率预测与含水率计算. 石油地球物理勘探. 2020(04): 864-872+704 .
4.	王谦，石玉江，谭茂金，李高仁. 基于孔隙结构分类的致密砂岩含水率计算模型——以鄂尔多斯盆地陇东西部延长组长8_1储层为例. 石油物探. 2019(05): 669-680 .

Other cited types(5)

Get Citation

PDF

XML

Article Metrics

Article views PDF downloads Cited by(9)

Research Progress and Development Prospect of Intelligent Surface Logging Technology

Abstract

References

Related Articles

Cited by

Periodical cited type(4)

Other cited types(5)

Catalog

Article Metrics

Related

Research Progress and Development Prospect of Intelligent Surface Logging Technology

Abstract

References

Related Articles

Cited by

Periodical cited type(4)

Other cited types(5)

Catalog

Article Metrics

Related

Export File

Citation

Format

Content