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An early warning model for the stuck-in medical drilling process based on the artificial fish swarm algorithm and SVM

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Abstract

To avoid the considerable challenges and losses caused by stuck drilling to normal drilling operations, this article analyses the mechanism of stuck drilling, then combines the artificial fish swarm algorithm (AFSA) and support vector machine (SVM), and finally proposes an early warning model for the stuck-in medical drilling process based on the AFSA and SVM. The model realizes real-time sticking risk warnings by using the four parameters of riser pressure, torque, speed and hook load collected in real time and promotes real-time drilling parameter monitoring for the real-time dynamic warning of sticking risk. By comparing the AFSA-SVM sticking prediction model with the particle swarm optimization model and the traditional cross-validation optimization model, it is found that the AFSA-SVM stuck prediction model accuracy can reach 97.561% and that the training and testing times are 5.874 s and 0.76 s, respectively. Its accuracy and computational efficiency are higher than those of the particle swarm optimization model and traditional cross-validation optimization model. In comparison with the existing technology, the four-parameter early sticking warning model based on AFSA-SVM presented in this paper shows powerful comprehensive performance and field application value.

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References

  1. Jiaqi, Yu.: Analysis of common stuck drill accidents in oil testing and fracturing. Chem Eng Equip. 03, 80–81 (2020). https://doi.org/10.19566/j.cnki.cn35-1285/tq.2020.03.035

    Article  Google Scholar 

  2. Wenjun, Z.: Study on the methods of relieving stuck in oil well workover operations. Chem Eng Design Commun 46(02), 259–260 (2020)

    Google Scholar 

  3. Hao, Su., Yiheng, Q.: Study on the treatment and preventive measures of stuck shale gas drilling. China Pet Chem Stand Qual 39(20), 26–27 (2019)

    Google Scholar 

  4. Annis, M.R., Monaghan, P.H.: Differential pressure stuck-laboratory studies of friction between steel and mud filter cake [J]. J. Petrol. Technol. 14(05), 537–543 (1962)

    Article  Google Scholar 

  5. Hempkins, W.B., Kingsborough, R.H., Lohec, W.E., et al.: Multivariate statistical analysis of stuck drillpipe situations [J]. SPE Dril Eng 2(03), 237–244 (1987)

    Article  Google Scholar 

  6. Biegler, M.W., Kuhn, G.R.: Advances in prediction of stuck pipe using multivariate statistical analysis. IADC/SPE Drilling Conference. OnePetro, 1994

  7. Zhigang, S., Weiguang, S., Juntao, C.: A Study on the Application of Artificial Neural Networks in Real-time Sticking Prediction. Geology and Prospecting. 02, 10–12 (2000)

    Google Scholar 

  8. Wang Yi. Design of an expert system for the diagnosis of stuck drill based on fuzzy theory[D]. Xi'an: Xi'an Shiyou University, 2007: 43–48.

  9. Jian, Hu., Yan, Hu.: neural network-based virtual sample sticking warning device. Oil Field Equipment 40(09), 80–83 (2011)

    Google Scholar 

  10. Vapnik, V.: The Nature of Statistical Learning Theory[M]. Springer-Verlag, New York (1995)

    Book  MATH  Google Scholar 

  11. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition[J]. Data Min. Knowl. Disc. 2(2), 121–167 (1998)

    Article  Google Scholar 

  12. Yao Zhenghua. Improved artificial fish school intelligent optimization algorithm and its application research. 2016. China University of Mining and Technology, PhD dissertation.

  13. Weiner, , et al.: Treatment and analysis of complex accidents of AD401–7 directional well stuck. Prosp Eng (Rock and Soil Drilling and Tunneling) 45(04), 10–16 (2018)

    Google Scholar 

  14. Jiming, S., et al.: Treatment of stuck drilling in deep well collapse with complicated trajectory offshore. Oil Drill Technol 38(04), 461–466 (2016). https://doi.org/10.13639/j.odpt.2016.04.011

    Article  Google Scholar 

  15. Gang, Luo, et al.: Sand bridge stuck drilling accident safety evaluation system. Pet Machine 41(05), 7-10+14 (2013)

    Google Scholar 

  16. Fayong, F.: Reverse circulation pressurization treatment method for sand bridge stuck in long horizontal wells. Petroleum Drilling Technology 39(06), 60–62 (2011)

    Google Scholar 

  17. Zhihua, Z.: Machine learning [M]. Tsinghua University Press, Beijing (2016)

    Google Scholar 

  18. Ping Yuan, Zhou Yajian, and Yang Yixian. Research on key technologies of clustering and text classification based on support vector machines. People's Posts and Telecommunications Publishing House.

  19. Li Xiaolei. A new intelligent optimization method-artificial fish swarm algorithm[D]. Zhejiang University, 2003.

  20. Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. (2011). https://doi.org/10.1145/1961189.1961199

    Article  Google Scholar 

  21. Jung, Y.: Multiple predicting K-fold cross-validation for model selection. J Nonparametr Stat (2017). https://doi.org/10.1080/10485252.2017.1404598

    Article  MATH  Google Scholar 

  22. Xinshe, Y., Deb, S., Fong, S.: Accelerated particle swarm optimization support vector machine for business optimization and application. Network digital technology. Springer, Berlin Heidelberg (2011)

    Google Scholar 

  23. Juxin, Hu., Gongjie, Z.: Selective ensemble classification algorithm based on K-fold cross-validation[J]. Bullet Sci Technol 29(12), 115–117 (2013)

    Google Scholar 

  24. Qinghua, Wang, Jiangwei, Liu, Lanlan, Zhang: Cross-validation K-nearest neighbor algorithm classification research [J]. J Xi’an Technol Univ 35(02), 119-124+141 (2015)

    Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Petroleum University, Chengdu, 610500, China

    Zhongyan Xian & Hai Yang

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  1. Zhongyan Xian
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  2. Hai Yang
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Correspondence to Hai Yang.

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Xian, Z., Yang, H. An early warning model for the stuck-in medical drilling process based on the artificial fish swarm algorithm and SVM. Distrib Parallel Databases 40, 779–796 (2022). https://doi.org/10.1007/s10619-021-07344-z

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