Volume 49 Issue 2
Apr.  2021
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GENG Lidong. Application Status and Development Suggestions of Big Data Technology in Petroleum Engineering[J]. Petroleum Drilling Techniques, 2021, 49(2): 72-78. DOI: 10.11911/syztjs.2020134
Citation: GENG Lidong. Application Status and Development Suggestions of Big Data Technology in Petroleum Engineering[J]. Petroleum Drilling Techniques, 2021, 49(2): 72-78. DOI: 10.11911/syztjs.2020134
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Application Status and Development Suggestions of Big Data Technology in Petroleum Engineering

  • Sinopec Research Institute of Petroleum Engineering, Beijing, 102206, China

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  • Received Date: June 11, 2020
  • Revised Date: December 24, 2020
  • Available Online: December 29, 2020
    Graphical Abstract
    • Abstract
  • Big data technology, gradually becoming one of the most important methods for oil companies and oilfield service companies to realize petroleum engineering intelligence, cost reduction and efficiency improvement, has become a research hotspot at home and abroad. The paper introduces the definition and characteristics of big data technology to promote its rapid development and widespread application in the oil and gas industry and accelerate the digital and intelligent transformation of petroleum engineering. In this study, the current application of big data in petroleum engineering is analyzed from two aspects: big data platforms and specific application scenarios including drilling, fracturing and developing. Based on the analysis, it is proposed that an unified big data platform should be established with the cooperation of digital giants to modify the data management mechanism and technical standards. Besides, basic and prospective research should be strengthened, and research projects should be implemented for specific application scenarios, establishing an ecosystem of big data for petroleum engineering. The application of big data technology in petroleum engineering is promising, and it is of great significance to accelerate the investigation and application of big data in petroleum engineering for promoting the economic and efficient development of oil and gas .
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    • References (24)
  • [1]
    MCKINSEY J, CHUI M, BROWN B, et al. Big data: the next frontier for innovation, competition, and productivity[EB/OL]. [2020-03-20]. https//www.mckinsey.com/business-functions/digital mckinsey/our-insights/big-data-the-next-frontier-for-innovation.
    [2]
    ISHWARAPPA, ANURADHA J. A brief introduction on big data 5Vs characteristics and hadoop technology[J]. Procedia Computer Science, 2015, 48: 319–324.
    [3]
    ZBOROWSKI M. How Conocophillips solved its big data problem[J]. Journal of Petroleum Technology, 2018, 70(7): 16–26.
    [4]
    AL-SUBAIEI D, AL-HAMER M, AL-ZAIDAN A, et al. Smart production surveillance: production monitoring and optimization using integrated digital oil field[R]. SPE 198114, 2019.
    [5]
    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. doi: 10.3969/j.issn.1002-302x.2018.04.006

    LIU Wei, YAN Na. Application and influence of artificial intelligence in petroleum engineering area[J]. Oil Forum, 2018, 37(4): 32–40. doi: 10.3969/j.issn.1002-302x.2018.04.006
    [6]
    DELFI cognitive E&P environment[EB/OL]. [2020-03-20]. https://www.software.slb.com/delfi.
    [7]
    GE’s predix[EB/OL]. [2020-03-20]. https://www.bhge.com/digital/ges-predix.
    [8]
    Halliburton Landmark introduces DecisionSpace® 365 cloud applications at annual innovation forum[EB/OL]. [2020-03-20]. https://www.halliburton.com/en-US/news/announcements/2019/halliburton-landmark-introduces-decisionSpace-365-cloud-applications.html?node-id=hgeyxtfs.
    [9]
    AKW Analytics Inc. and PALM- Petroleum Analytics Learning MachineTM[DB/OL]. [2020-03-20]. https://www.researchgate.net/publication/335276595_AKW_Analytics_Inc.
    [10]
    新华网. 中石油发布勘探开发梦想云平台[EB/OL]. [2020-03-20]. http://www.xinhuanet.com//fortune/2018-11/27/c_1123775741.htm.

    Xinhuanet. PetroChina published the dream cloud platform for exploration and exploitation[EB/OL].[2020-03-20]. http://www.xinhuanet.com//fortune/2018-11/27/c_1123775741.htm.
    [11]
    BUSBY D, PIVOT F, TADJER A. Use of data analytics to improve well placement optimization under uncertainty[R]. SPE 188265, 2017.
    [12]
    LASHARi S E, TAKBIRI-BORUJENI A, FATHI E, et al. Drilling performance monitoring and optimization: a data-driven approach [J]. Journal of Petroleum Exploration and Production Technology, 2019, 9(4): 2747–2756.
    [13]
    侯凯．基于大数据的钻头选型方法研究[D]．成都: 西南石油大学, 2018．

    HOU Kai. Investigation of bit selection method based on the big data[D]. Chengdu: Southwest Petroleum University, 2018.
    [14]
    NOSHI C, SCUBERT J J. Application of data science and machine learning algorithms for ROP optimization in West Texas: turing data into knowledge[R]. OTC 29288, 2019.
    [15]
    左迪一．基于大数据分析的克深区块钻井综合提速研究[D]．北京: 中国石油大学（北京）, 2018．

    ZUO Diyi. Research on ROP increasing in Keshen Block based on big data analysis[D]. Beijing: China University of Petroleum (Beijing), 2018.
    [16]
    刘胜娃, 孙俊明, 高翔, 等．基于人工神经网络的钻井机械钻速预测模型的分析与建立[J]．计算机科学, 2019, 46（增刊1）: 605–608．

    LIU Shengwa, SUN Junming, GAO Xiang, et al. Analysis and establishment of drilling speed prediction model for drilling machinery based on artificial neural networks[J]. Computer Science, 2019, 46(supplement 1): 605–608.
    [17]
    GUPTA I, TRAN N, DEVEGOWDA D, et al. Looking ahead of the bit using surface drilling and petrophysical data: machine-learning-based real time geosteering in volve field[R]. SPE 199882, 2020.
    [18]
    李维校．基于石油钻井大数据技术的钻进优化控制的研究[D]．西安: 西安石油大学, 2018．

    LI Weixiao. Research on drilling optimization control based on petroleum drilling big data technology[D]. Xi’an: Xi’an Shiyou University, 2018.
    [19]
    JOHNSTON J, GUICHARD A. New findings in drilling and wells using big data analytics[R]. OTC 26021, 2015.
    [20]
    RAED A, MOHAMMAD A, SALEM G, et al. Drilling through data: automated kicked detection using data mining[R]. SPE 193687, 2018.
    [21]
    PANKAJ P, GEETAN S, MACDONALD R, et al. Need for speed: data analytics coupled to reservoir characterization fast tracks well completion optimization[R]. SPE 189790, 2018.
    [22]
    LIANG Yu, LIAO Lulu, GUO Ye. A big data study: correlations between EUR and petrophysics/engineering/production parameters in shale formations by data regression and interpolation analysis[R]. SPE 194381, 2019.
    [23]
    WILSON A. Technique blends dimensionless numbers and data mining to predict recovery factors[J]. Journal of Petroleum Technology, 2017, 69(10): 88–90.
    [24]
    ROLLINS B T, BROUSSARD A, CUMMINS B, et al. Continental production allocation and analysis through big data[R]. URTEC 2678296, 2017.
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