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Data Analytics for Drilling Engineering pp 279–312Cite as

Prospect of Big Data Application in Drilling Engineering

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Part of the book series: Information Fusion and Data Science ((IFDS))

Abstract

The big data is often called the petroleum in the information era, but the connection between petroleum and big data is not limited to it. The big data technology and petroleum and natural gas industry are closely related. In the economic environmental background where the global energy market is gloomy, the petroleum and natural gas companies increasingly obviously pay close attention to the big data. Not merely the big data technology will generate influences on the oil & gas industry. The progresses obtained in computing technology, Internet of Things, cloud computing, mobile communication technology, robot technology and artificial intelligence bring new innovations for the oil & gas industry. Integrating the traditional production mode in the oil & gas industry with the rapidly developing Internet industry will definitely make the oil & gas industry glow the new vitality.

Contributions by Fangtao Li and Qilong Xue.

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References

  1. Economist A. Special report on managing information: data, data everywhere. Economist[J]. 2010.

    Google Scholar 

  2. Mark, “Gartner says solving ‘big data’ challenge involves more than just managing volumes of data,” Gartner. Archived from the original on Jul. 10, 2011, Retrieved Jul. 13, 2011.

    Google Scholar 

  3. Mohammadpoor M, Torabi F. Big data analytics in oil & gas industry: an emerging trend[J]. Petroleum, 2018.

    Google Scholar 

  4. Feblowitz J. The big deal about big data in upstream oil &gas[J]. IDC Energy Insights, 2012: 1–11.

    Google Scholar 

  5. Baaziz A, Quoniam L. How to use Big Data technologies to optimize operations in Upstream Petroleum Industry[J]. arXiv preprint arXiv:1412.0755, 2014.

    Google Scholar 

  6. Jin W, Li ZJ, Wei LS, et al. The improvements of BP neural network learning algorithm[C]//WCC 2000-ICSP 2000. 2000 5th international conference on signal processing proceedings. 16th world computer congress 2000. IEEE, 2000, 3: 1647–1649.

    Google Scholar 

  7. Ben-Haim Y. Tom-Tov E. A streaming parallel decision tree algorithm[J]. J Mach Learn Res. 2010;11(Feb):849–72.

    MathSciNet  MATH  Google Scholar 

  8. Joachims T. Text categorization with support vector machines: learning with many relevant features[C]//European conference on machine learning. Berlin: Springer; 1998. p. 137–42.

    Google Scholar 

  9. Manikandan SG, Ravi S. Big data analysis using Apache Hadoop[C]//2014 international conference on IT convergence and security (ICITCS). IEEE, 2014: 1–4.

    Google Scholar 

  10. Perrons RK, Jensen JW. Data as an asset: what the oil & gas sector can learn from other industries about “Big Data”[J]. Energy Policy. 2015;81:117–21.

    Article  Google Scholar 

  11. Feblowitz J. Analytics in oil & gas: the big deal about big data[C]//SPE digital energy conference. Society of Petroleum Engineers, 2013.

    Google Scholar 

  12. Mohammadi AH, Anderson R, Tohidi B. Carbon monoxide clathrate hydrates: equilibrium data and thermodynamic modeling[J]. AICHE J. 2005;51(10):2825–33.

    Article  Google Scholar 

  13. Xie H, Shanmugam AK, Issa RRA. Big data analysis for monitoring of kick formation in complex underwater drilling projects[J]. J Comput Civ Eng. 2018;32(5):04018030.

    Article  Google Scholar 

  14. Qiang X, Cheng G. An overview of China intelligent digital oilfields development in 2017[C]//2017 International Conference Advanced Engineering and Technology Research (AETR 2017). Atlantis Press, 2018.

    Google Scholar 

  15. Kragas TK, Williams BA, Myers GA. The optic oil field: deployment and application of permanent in-well fiber optic sensing systems for production and reservoir monitoring[C]//SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2001.

    Google Scholar 

  16. Kersey AD. Optical fiber sensors for permanent downwell monitoring applications in the oil & gasindustry[J]. IEICE Trans Electron. 2000;83(3):400–4.

    Google Scholar 

  17. Aref SH, Latifi H, Zibaii MI, et al. Fiber optic Fabry–Perot pressure sensor with low sensitivity to temperature changes for downhole application[J]. Opt Commun. 2007;269(2):322–30.

    Article  Google Scholar 

  18. Bertocco R, Padmanabhan V. Big data analytics in oil &gas[J]. Bain Brief, March, 2014.

    Google Scholar 

  19. Rubinstein JL, Mahani AB. Myths and facts on wastewater injection, hydraulic fracturing, enhanced oil recovery, and induced seismicity[J]. Seismol Res Lett. 2015;86(4):1060–7.

    Article  Google Scholar 

  20. Noshi CI, Assem AI, Schubert JJ. The role of big data analytics in exploration and production: a review of benefits and applications[C]//SPE international heavy oil conference and exhibition. Society of Petroleum Engineers, 2018.

    Google Scholar 

  21. Hassani H, Silva ES. Big data: a big opportunity for the petroleum and petrochemical industry[J]. OPEC Energy Rev. 2018;42(1):74–89.

    Article  Google Scholar 

  22. Baaziz A, Quoniam L. The information for the operational risk management in uncertain environments: Case of early kick detection while drilling of the oil or gas wells[J]. Int J Innov Appl Stud. 2013;4(1)

    Google Scholar 

  23. Thorogood J, Aldred WD, Florence F, et al. Drilling automation: technologies, terminology, and parallels with other industries[J]. SPE Drill Complet. 2010;25(04):419–25.

    Article  Google Scholar 

  24. Macpherson JD, de Wardt JP, Florence F, et al. Drilling-systems automation: current state, initiatives, and potential impact[J]. SPE Drill Complet. 2013;28(04):296–308.

    Article  Google Scholar 

  25. Michel J, Owens EH, Zengel S, et al. Extent and degree of shoreline oiling: deepwater horizon oil spill, Gulf of Mexico, USA[J]. PLoS One. 2013;8(6):e65087.

    Article  Google Scholar 

  26. Hems A, Soofi A, Perez E. How innovative oil & gas companies are using big data to outmaneuver the competition[J]. Microsoft White Pap., 2013.

    Google Scholar 

  27. Baihly JD, Altman RM, Malpani R, et al. Shale gas production decline trend comparison over time and basins[C]//SPE annual technical conference and exhibition. Society of Petroleum Engineers, 2010.

    Google Scholar 

  28. Rahuma KM, Mohamed H, Hissein N, et al. Prediction of reservoir performance applying decline curve analysis[J]. Int J Chem Eng Appl. 2013;4(2):74.

    Google Scholar 

  29. Dong C, Mullins OC, Hegeman PS, et al. In-situ contamination monitoring and GOR measurement of formation fluid samples[C]//SPE Asia Pacific oil & gas conference and exhibition. Society of Petroleum Engineers, 2002.

    Google Scholar 

  30. Anderson RN. ‘Petroleum analytics learning machine’ for optimizing the internet of things of today’s digital oil field-to-refinery petroleum system[C]//2017 IEEE international conference on big data (big data). IEEE, 2017: 4542–4545.

    Google Scholar 

  31. Zikopoulos P, Eaton C. Understanding big data: analytics for enterprise class hadoop and streaming data[M]. McGraw-Hill Osborne Media, 2011.

    Google Scholar 

  32. Bolen M, Crkvenjakov V, Converset J. The role of big data in operational excellence and real time Fleet performance management—the key to Deepwater thriving in a low-cost oil environment[C]//IADC/SPE drilling conference and exhibition. Society of Petroleum Engineers, 2018.

    Google Scholar 

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Authors and Affiliations

  1. China University of Geosciences, Beijing, China

    Qilong Xue

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  1. Qilong Xue
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Xue, Q. (2020). Prospect of Big Data Application in Drilling Engineering. In: Data Analytics for Drilling Engineering. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-030-34035-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-34035-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34034-6

  • Online ISBN: 978-3-030-34035-3

  • eBook Packages: EngineeringEngineering (R0)

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