A deviation correction strategy based on particle filtering and improved model predictive control for vertical drilling. (May 2021)

• Record Type: Journal Article
• Authors: Zhang, Dian; Wu, Min; Lu, Chengda; Chen, Luefeng; Cao, Weihua
• Is Part Of: ISA transactions. Volume 111(2021)
• Page Start: 265
• Page End: 274
• Publication Date: 2021-05
• Subjects: Geological exploration -- Vertical drilling -- Deviation correction -- Particle filter -- Improved model predictive control; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.11.023
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper is concerned with the correction of trajectory deviation in vertical drilling. Note that the accuracy of correction control will be reduced significantly by measurement and process noises, which finally leads to that the inclination angle exceeds beyond a tolerable limit. To deal with such noises and take into account practical constraints, a deviation correction strategy is developed for vertical drilling based on particle filtering and improved model predictive control in this paper. Firstly, the distributions and characters of the measurement and process noises in vertical drilling process are analyzed, and their approximate prior probability distributions are obtained. Based on the analysis, the structure of the deviation correction strategy is provided, including a particle filter and an improved model predictive controller which introduces a flexible constraint and an adjustable weight. The particle filter is effective to reject the measurement noises, and the improved model predictive controller plays an important role in achieving a small inclination of the drilling trajectory. Finally, two groups of simulations are carried out to illustrate the effectiveness of the proposed correction strategy. Highlights: A correction strategy is developed for vertical drilling subject to the noises. A particle filter is introduced to acquire quality parameters for the controller. A flexible constraint is used to ensure feasibility of MPC during correction. An adjustable weight is proposed to change the priority of control appropriately.