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A deviation correction strategy based on particle filtering and improved model predictive control for vertical

Dian Zhang1, Min Wu1, Chengda Lu1

  • 1School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China.

ISA Transactions
|December 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new strategy to correct trajectory deviations in vertical drilling, effectively managing measurement and process noises to maintain a small inclination angle using particle filtering and improved model predictive control.

Keywords:
Deviation correctionGeological explorationImproved model predictive controlParticle filterVertical drilling

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Area of Science:

  • Geotechnical Engineering
  • Control Systems Engineering

Background:

  • Trajectory deviation in vertical drilling is a significant challenge.
  • Measurement and process noises reduce control accuracy, leading to excessive inclination angles.

Purpose of the Study:

  • To develop a robust deviation correction strategy for vertical drilling.
  • To mitigate the impact of noise and practical constraints on drilling accuracy.

Main Methods:

  • Analysis of measurement and process noise distributions in vertical drilling.
  • Implementation of a particle filter for noise rejection.
  • Application of an improved model predictive controller with flexible constraints and adjustable weights.

Main Results:

  • The particle filter effectively filters out measurement noises.
  • The improved model predictive controller successfully minimizes drilling trajectory inclination.
  • Simulations demonstrate the effectiveness of the proposed correction strategy.

Conclusions:

  • The proposed strategy enhances the accuracy of trajectory correction in vertical drilling.
  • The combination of particle filtering and improved model predictive control offers a viable solution for noise-affected drilling operations.