Skip to main content
SpringerLink
Log in

Overflow remote warning using improved fuzzy c-means clustering in IoT monitoring system based on multi-access edge computing

  • Multi-access Edge Computing Enabled Internet of Things
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In existing overflow remote intelligent monitoring system, a huge amount of data uploading and multiple processing brings great challenges to the bandwidth load and real-time feedback of the server. Based on the Multiple Access Edge Computing Architecture (MEC), this paper proposes an Internet of Things overflow intelligent monitoring system based on multi-access edge computing. As the middle layer of the system, edge computing can provide real-time local services for field devices, and it can reduce the data uploading amount by preliminarily analyzing the computing tasks of the cloud computing platform. At the same time, for the current domestic and international artificial intelligence-based overflow warning model, it needs a large amount of prior knowledge or training data before use, and the accuracy, real time, and reliability of overflow monitoring are limited by prior knowledge and training data and other issues. In this paper, the information entropy theory has been adopted to improve fuzzy c-means clustering (FCM) algorithm to overcome the disadvantage that the user gives the number of clustering actively in FCM clustering. Then, considering the correlation between the occurrence of overflow accident and the changing trend of standpipe pressure and casing pressure, an intelligent early warning model of drilling overflow accident is proposed by using the improved FCM clustering method based on information entropy. The early warning model uses the adaptive determination of the number of clusters for clustering, which not only ensures the quality of the cluster but also improves the accuracy and reliability of the overflow warning. The warning result of the overflow accident is output according to the clustering fitting result and the sensitivity of the overflow accident. Finally, the drilling data of YY oil well in XX oilfield considered as the research object.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Chen P (2005) Drilling and well completion engineering. Petroleum Industry Press, Beijing

    Google Scholar 

  2. Liang H, Sun Y, Li G et al (2019) Gray relational clustering model for intelligent guided monitoring horizontal wells. Neural Comput Appl 31:1339–1351. https://doi.org/10.1007/s00521-018-3645-4

    Article  Google Scholar 

  3. Bader A, Ghazzai H et al (2016) Front-end intelligence for large-scale application-oriented internet-of-things. IEEE Access 4:3257–3272. https://doi.org/10.1109/ACCESS.2016.2580623

    Article  Google Scholar 

  4. Pawani P, Jude O, Madhusanka L et al (2018) Survey on multi-access edge computing for internet of things realization. IEEE Commun Surv Tutor 20(99):2961–2991. https://doi.org/10.1109/COMST.2018.2849509

    Article  Google Scholar 

  5. Premsankar G, DiFrancesco M et al (2018) Edge computing for the internet of things: a case study. IEEE Internet Things J 5:1275–1284. https://doi.org/10.1109/JIOT.2018.2805263

    Article  Google Scholar 

  6. Liang H, Zou J, Khan MJ et al (2019) An sand plug of fracturing intelligent early warning model embedded in remote monitoring system. IEEE Access 7:47944–47954. https://doi.org/10.1109/ACCESS.2019.2909647

    Article  Google Scholar 

  7. Deng HX, Wei GH, Li JL et al. (2018) An overflow intelligent early-warning model based on downhole parameters measurement. In: International conference on image and video processing, and artificial intelligence. International Society for Optics and Photonics 10836: 1083624. https://doi.org/10.1117/12.2514042

  8. Liang H, Zou J, Liang W (2017) An early intelligent diagnosis model for drilling overflow based on GA–BP algorithm. Cluster Comput 2:1–20. https://doi.org/10.1007/s10586-017-1152-5

    Article  Google Scholar 

  9. Abonyi J, Szeifert F (2003) Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit Lett 24(14):2195–2207. https://doi.org/10.1016/s0167-8655(03)00047-3

    Article  MATH  Google Scholar 

  10. Zhao Z et al (2018) Deploying edge computing nodes for large-scale IoT: a diversity aware approach. IEEE Internet Things J 5:3606–3614. https://doi.org/10.1109/JIOT.2018.2823498

    Article  Google Scholar 

  11. Cheng B, Papageorgiou A, Cirillo F, et al. (2015) GeeLytics: geo-distributed edge analytics for large scale IoT systems based on dynamic topology. IEEE World Forum Internet Things, WF-IoT 2015—Proc., no 12, pp 565–570. https://doi.org/10.1109/WF-IoT.2015.7389116

  12. Reitsma D (2010) A simplified and highly effective method to identify influx and losses during managed pressure drilling without the use of a Coriolis flow meter. SPE/IADC managed pressure drilling and underbalanced operations conference and exhibition. Society of Petroleum Engineers, SPE-130312-MS. https://doi.org/10.2118/130312-MS

  13. Reitsma D (2011) Development of an automated system for the rapid detection of drilling anomalies using standpipe and discharge pressure. In: SPE/IADC drilling conference and exhibition. Society of Petroleum Engineers, SPE-140255-MS. https://doi.org/10.2118/140255-MS

  14. Mills I et al. (2012) Simulator and the first field test results of an automated early kick detection system that uses standpipe pressure and annular discharge pressure. In: SPE/IADC managed pressure drilling and underbalanced operations conference and exhibition. Society of Petroleum Engineers, SPE-156902-MS. https://doi.org/10.2118/156902-MS

  15. Liang J, Zhong ZS (2004) The information entropy, rough entropy and knowledge granulation in rough. Int J Uncertain Fuzziness Knowl Based Syst 12:37–46. https://doi.org/10.1142/S0218488504002631

    Article  MathSciNet  MATH  Google Scholar 

  16. Tilson LV, Excell PS, Green RJ (1988) A generalisation of the fuzzy c-means clustering algorithm. In: International geoscience and remote sensing symposium, ‘Remote Sensing: Moving Toward the 21st Century’. 3:1783–1784. https://doi.org/10.1109/IGARSS.1988.569600

  17. Bezdek James C et al (1987) Convergence theory for fuzzy C-Means: counter-examples and repairs. IEEE Trans Syst Man Cybern 17:873–877. https://doi.org/10.1109/TSMC.1987.6499296

    Article  MATH  Google Scholar 

  18. Gan HT et al (2013) Using clustering analysis to improve semi-supervised classification. Neurocomputing 101:290–298. https://doi.org/10.1016/j.neucom.2012.08.020

    Article  Google Scholar 

  19. Fu Y, Wu XP et al (2010) An approach for information systems security risk assessment on fuzzy set and entropy-weight. Acta Electronica Sinica 38:1489–1494. https://doi.org/10.1007/978-3-642-13199-8_2

    Article  Google Scholar 

  20. Cai W, Chen S et al (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40:825–838. https://doi.org/10.1016/j.patcog.2006.07.011

    Article  MATH  Google Scholar 

  21. Jia JX, Qing HL, Jing T (2008) A heuristic clustering algorithm for intrusion detection based on information entropy. Wuhan Univ J Nat Sci 11:355–359. https://doi.org/10.1007/bf02832121

    Article  Google Scholar 

  22. Bezdek JC (1980) A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans Pattern Anal Mach Intell 2:1–8. https://doi.org/10.1109/TPAMI.1980.4766964

    Article  MATH  Google Scholar 

  23. Bezdek JC, Pal NR (1995) Two soft relatives of learning vector quantization. Neural Netw 8:729–743. https://doi.org/10.1016/0893-6080(95)00024-t

    Article  Google Scholar 

  24. Karayiannis NB (1994) MECA: maximum entropy clustering algorithm. In: Proceedings of 1994 IEEE 3rd international fuzzy systems conference. pp 630–635. https://doi.org/10.1109/FUZZY.1994.343658

  25. Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26:354–359. https://doi.org/10.1093/comjnl/26.4.354

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Haibo Liang, Gang Liu & Muhammad Junaid Khan

  2. China National Offshore Oil Corporation Tianjin Branch, Tianjin, 300450, China

    Jianchong Gao

Authors
  1. Haibo Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianchong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Junaid Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haibo Liang.

Ethics declarations

Conflict of interest

To the best of our knowledge, the named authors have no conflict of interest, financial or otherwise.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, H., Liu, G., Gao, J. et al. Overflow remote warning using improved fuzzy c-means clustering in IoT monitoring system based on multi-access edge computing. Neural Comput & Applic 32, 15399–15410 (2020). https://doi.org/10.1007/s00521-019-04540-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04540-y

Keywords

Search

Navigation