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Drilling Stuck Pipe Prediction in Algerian Oil Fields: Time Series Forecasting Approach

  • Ahmed GhenabziaEmail author
  • Okba KazarEmail author
  • Abdelhak MerizigEmail author
  • Zaoui SayahEmail author
  • Merouane Zoubeidi
Conference paper
  • 31 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1104)

Abstract

The Companies specialized in oil drilling field make to reduce the cost of digging and optimize the operation time. The Drill string stuck (stuck pipe) is the biggest frequent problem during drilling operation which imposes rises to the drilling cost of services.

The main goal of the drillers is the minimization of the Drilling Time and Reduce the Drilling Cost with avoiding the Drilling Problems (e.g. Stuck pipe). They are trying to use new methods based on artificial intelligence to solve these problems. Generally, the stuck pipe is discovered too late after the accident. The aim of this study is trying to predict this problem before happening which is possible to provide the best solution to limit the consequences and avoid the danger and its financial loss.

To solve this problem, it is more convenient to propose a new architecture based on time series forecasting for analyzing the huge Algerian oils fields drilling datasets and implement it in the Hadoop Ecosystem.

Keywords

Stuck pipe Hadoop Big data Time series forecasting Artificial intelligence 

Notes

Acknowledgement

This work is supported by Laboratory LINATI (Kasdi Merbah University, Ouargla, Algeria), Laboratory LINFI (Mohamed Khider University, Biskra, Algeria) and SONATRACH company represented by ENAFOR, ENTP and ENSP.

References

  1. 1.
    Eren, T.: Real-time-optimization of drilling parameters during drilling operations. Middle East Tech. University, p. 165 (2010)Google Scholar
  2. 2.
    Drilling for Crude Oil - The Drilling Rig: Revision notes for GSCE Chemistry. http://www.passmyexams.co.uk/GCSE/chemistry/drilling-crude-oil-1.html. Accessed 27 Aug 2018
  3. 3.
    Laney, D.: 3D Data Management: Controlling Data Volume, Velocity, and Variety (2001)Google Scholar
  4. 4.
    Landset, S., Khoshgoftaar, T.M., Richter, A.N., Hasanin, T.: A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J. Big Data 2(1), 24 (2015)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Frank, R.J., Davey, N., Hunt, S.P.: Time series prediction and neural networks. J. Intell. Robot. Syst. 31, 91–103 (2001)CrossRefGoogle Scholar
  7. 7.
    Nezhad, M.M., Ashoori, S., Hooshmand, P., Mirzaee, M.: Stuck drill pipe prediction with networks neural in maroon field. J. Basic Appl. Sci. Res. 2(6), 5570–5575 (2012)Google Scholar
  8. 8.
    Zoveidavianpoor, M.: Drilling stuck pipe prediction in Iranian oil fields: an artificial neural network approach (2010)Google Scholar
  9. 9.
    Drilling Software and Services. https://www.software.slb.com/products/disciplines/drillingsoftware. Accessed 27 Aug 2018
  10. 10.
    Merizig, A., Kazar, O., Lopez-Sanchez, M.: A dynamic and adaptable service composition architecture in the cloud based on a multi-agent system. Int. J. Inf. Technol. Web Eng. (IJITWE) 13(1), 50–68 (2018)CrossRefGoogle Scholar
  11. 11.
    Merizig, A., Saouli, H., Zouai, M., Kazar, O.: An intelligent approach for enhancing the agricultural production in arid areas using IoT technology. In: International Conference on Advanced Intelligent Systems for Sustainable Development, July 2018, pp. 22–36. Springer, Cham (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LINATI LaboratoryKasdi Merbah UniversityOuarglaAlgeria
  2. 2.LINFI LaboratoryMohamed Khider UniversityBiskraAlgeria
  3. 3.ENSP, HASSI MESAOUDOuarglaAlgeria

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