Experiment and result analysis of multi-object intelligent optimization algorithm for 3D sidetracking trajectory

doi:10.12143/j.ztgc.2022.04.004

Home > Archive>Volume 49, Issue 4, 2022 >23-30. DOI:10.12143/j.ztgc.2022.04.004

Experiment and result analysis of multi-object intelligent optimization algorithm for 3D sidetracking trajectory
DOI:
                        10.12143/j.ztgc.2022.04.004
                    
CSTR:
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Author:
                        HUANG Wendi1,2,3HUANG Wendi
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
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HU Jie1,2,3HU Jie
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
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LU Chengda1,2,3LU Chengda
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
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WU Min1,2,3WU Min
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
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Affiliation:1.School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;3.Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
Clc Number:P634
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Abstract:

Multi-object optimization of drilling trajectory is one of the keys to achieve efficient and safe drilling. The improvement of the optimization algorithms can get smaller trajectory length and less complexity in finding solutions to the trajectory optimization questions. However， the practicability of the trajectory optimization algorithm has not been verified. In this paper， the multi-object decomposition evolutionary algorithm combined with the adaptive penalty function， and the comprehensive evaluation method based on minimum fuzzy entropy are used to study the 3D sidetracking well trajectory optimization problem. The proposed methods were verified for their practicability with application in the drilling process intelligent control experimental system. They can provide reference and guidance for trajectory design in engineering practice， and provide reference for drilling trajectory tracking control.

Key words:directional well;3D sidetracking well;drilling trajectory;trajectory optimization;multi-objective optimization

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History

Received:May 07,2022
Revised:June 14,2022
Adopted:June 16,2022
Online: July 18,2022
Published: July 10,2022

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