JACIII Vol.25 No.1 pp. 23-30
doi: 10.20965/jaciii.2021.p0023


Intelligent Compensating Method for MPC-Based Deviation Correction with Stratum Uncertainty in Vertical Drilling Process

Dian Zhang*,**,***, Min Wu*,**,***,†, Chengda Lu*,**,***, Luefeng Chen*,**,***, Weihua Cao*,**,***, and Jie Hu*,**,***

*School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China

***Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China

Corresponding author

October 2, 2020
October 13, 2020
January 20, 2021
model predictive control, deviation correction control, vertical drilling, intelligent compensating method

With the rapid development of control technology, increasing applications are using model predictive control (MPC) for deviation correction in vertical drilling. However, the accuracy of the predictive model is affected by the uncertainty of the stratum, which results in model mismatch and a reduction in control performance. In this paper, an intelligent compensating method is proposed for MPC-based deviation correction with stratum uncertainty in a vertical drilling process to increase control accuracy. First, a trajectory extension model is introduced as the predictive model for MPC, and the uncertainty of the stratum is discussed. Then, the compensation for the MPC is acquired based on a Gaussian fitting method and hybrid bat algorithm. Finally, based on the actual drilling data, a simulation is performed to demonstrate the effectiveness of the proposed method.

Flowchart of the compensation method

Flowchart of the compensation method

Cite this article as:
D. Zhang, M. Wu, C. Lu, L. Chen, W. Cao, and J. Hu, “Intelligent Compensating Method for MPC-Based Deviation Correction with Stratum Uncertainty in Vertical Drilling Process,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.1, pp. 23-30, 2021.
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Last updated on Jun. 03, 2024