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, Volume 78, Issue 21, pp 29823–29851 | Cite as

Neural network prediction model to achieve intelligent control of unbalanced drilling’s underpressure value

  • Liang Haibo
  • Li ZhenglinEmail author
  • Li Guoliang
Article
  • 89 Downloads

Abstract

In underbalanced drilling, accidents like well leakage and overflow not only damage the reservoir but cause great safety risks to drilling operations. Therefore, it is of great engineering significance to maintain a reasonable under-pressure state by controlling a reasonable underpressure value. Data mining is an advanced method for retrieving and creating corresponding models in massive data. During the drilling process, there are a large number of real-time monitoring data and historical data. Therefore, a neural network prediction control model based on improved rolling optimization algorithm has been proposed. Combined with control principle of underpressure value, a set of online rolling optimization neural network control model for achieving underpressure value intelligent control of underbalanced drilling is formed. The control model optimizes the neural network prediction control model through the rolling optimization algorithm, realizing advanced prediction of reasonable underpressure value, and performs fast and stable self-feedback control of the output prediction results. By using field data for optimization analysis, the analysis results show that using the neural network prediction control model of online rolling optimization can effectively conduct accurate prediction and real-time control for the reasonable underpressure value.

Keywords

Underpressure value Prediction control Rolling optimization, RBF neural network, control model 

Notes

Acknowledgements

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

Compliance with ethical standards

Conflicts of interest

None.

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Copyright information

© 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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