Multimedia Tools and Applications

, Volume 78, Issue 21, pp 29805–29821 | Cite as

Implement intelligent dynamic analysis of bottom-hole pressure with naive Bayesian models

  • Zhang He
  • Tan YunEmail author


During the drilling process, measured bottom-hole pressure data were prone to distortion and even no data are fed back, besides, the bottom-hole pressure calculation model could not reflect the live measured values. As a consequence, inaccurate bottom-hole pressure monitoring would bring enormous safety risks to drilling operations. Data mining was an advanced method used to sort out, discover and set up models from large relevant data sets. In the monitoring of bottom-hole pressures, it was necessary to conduct an effective and overall monitoring during the drilling process. Therefore, this paper proposed the k-means clustering method to optimize Naive Bayesian models in combination with the bottom-hole pressure monitoring theory, a k-means clustering optimized Naive Bayesian model for implementation of intelligent dynamic analysis of bottom-hole pressure was established. Such model could be utilized for correcting bottom-hole pressures calculated by the traditional hydraulic model, and then the corrections were taken for comparison with measured bottom-hole pressures so as to make calculations be of minimal errors. Field data were also taken for analysis, and the results suggested that the k-means based Naive Bayesian models for correction to calculated bottom-hole pressures had smaller deviations which fell within safe deviation monitoring range of drilling pressures, and could meet the requirements of normal drilling operation.


Bottom-hole pressure K-means clustering Naive bayes Hydraulic model Correction 



This work wos supported by the Young Scholars Development Found of SWPU(No.201599010079) and Sichuan Province Applied Basic Research Project(No.2016JY0049).


  1. 1.
    Chunguang B, Guifen C (2010) Study on the Naïve Bayes Algorithm Application Based on Data Mining, Agriculture Network Information, (3):19–22Google Scholar
  2. 2.
    Guojun M (2011) Principles and algorithms for data mining, Tsinghua University PressGoogle Scholar
  3. 3.
    Haibo L, Xinrong L, Yu W et al (2012) Amending Technology for Drilling Pressure model based on neural network. Automation & Instrumentation 27(11):9–11Google Scholar
  4. 4.
    Haibo L, Yongqiang T, Xiang L et al (2014) Research on drilling kick and loss monitoring method based on Bayesian classification. Pak J Statist 30(6):1251–1266Google Scholar
  5. 5.
    Hao W, Yaojun D (2014) Naive Bayes classification algorithm based on attribute correlation. Henan Science 31(01):42–46Google Scholar
  6. 6.
    He J, Zhang Y, Li X et al (2012) Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty[J]. Int J Syst Sci 43(10):1805–1825MathSciNetCrossRefGoogle Scholar
  7. 7.
    Hongfeng Z (2012) Exploration of well control technology in oil field. China New Technologies and Products (07):127–127Google Scholar
  8. 8.
    Huanqiang Q (2017) Simple analysis of naive bayes classification algorithm, Fujian Quality Management, (17):235Google Scholar
  9. 9.
    Jiang L, Cai Z, Wang D (2010) Improving naive Bayes for classification. Int J Comput Appl 32(3):328–33Google Scholar
  10. 10.
    Jiang L, Cai Z, Zhang H et al (2013) Naive Bayes text classifiers: a locally weighted learning approach[J]. Journal of Experimental & Theoretical Artificial Intelligence 25(2):273–286CrossRefGoogle Scholar
  11. 11.
    Jianhong F (2015) Key Techniques of Crowdsourced Qurery Processing, Tsinghua University, Beijing, pp 16–38Google Scholar
  12. 12.
    Karakostas B (2016) Event Prediction in an IoT Environment Using Naïve Bayesian Models. Procedia Computer Science 83:11–17CrossRefGoogle Scholar
  13. 13.
    Menggang L, Wang C, Binzhen B (2008) Present situation and application prospect of pressure survey while drilling. Fault-Block Oil & Gas Field 15(6):123–126Research on Optimized Naïve Google Scholar
  14. 14.
    Ming D, Caiming Z, Yongming P, Kai C (2016) Clustering algorithm recommendation based on dataset attributes similarity, Journal of Nanjing University(Natural Sciences), (05):908–917Google Scholar
  15. 15.
    SY/T 6613-2005 Recommended practice on the rheology and hydraulics of oil-well drilling fluidsGoogle Scholar
  16. 16.
    Taheri S, Yearwood J, Mammadov M et al (2014) Attribute weighted naive Bayes classifier using a local optimization. Neural Computing & Applications 24(5):995–1002CrossRefGoogle Scholar
  17. 17.
    Tiantai L, Zhengyi S, Qi L. (2002) Practical drilling hydraulics calculation and application, Petroleum Industry Press, 74–87Google Scholar
  18. 18.
    Wentao Z, Lingjun M, Haohao Z et al. (2016) Improvement and Application of the Naïve Algorithm, Measurement & Control Technology, (2):143–147Google Scholar
  19. 19.
    Xiangjie P, Hongsheng T, Peng C (2015) Research on Optimized Naïve Bayesian Algorithm in SMS Spam Filtering. Computer Technology and Development 25(9):89–93Google Scholar
  20. 20.
    Xiaojun B, Wei P (2010) Improved Bayesian optimization algorithm based on immune algorithm. Chinese Journal Of Scientific Instrument 31(10):2368–2373Google Scholar
  21. 21.
    Xiaoxi Y (2015) The Application of Association and Clustering Analysis in Data Mining, Yunnan UniversityGoogle Scholar
  22. 22.
    Yan C (2016) Data Mining Technology and Application. Tsinghua University PressGoogle Scholar
  23. 23.
    Yan Z (2016) Research on Mining Methods for Big Data. University of Electronic Science and Technology PressGoogle Scholar
  24. 24.
    Zai S (2011) Research and application of pressure while drilling measurement technology, China Science and Technology Information, (9):81–82Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanic EngineeringSouthwest Petroleum UniversityChengduChina

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