Metaheuristic Algorithm for Wellbore Trajectory Optimization
A variety of possible well types and so many complex drilling variables and constraints make the wellbore optimization problem a very challenging work. Several types of well are listed as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reac...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079329660&doi=10.1109%2fICECIE47765.2019.8974724&partnerID=40&md5=a3254de2e358ef0c4e09d26d827b706c http://eprints.utp.edu.my/23532/ |
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| Summary: | A variety of possible well types and so many complex drilling variables and constraints make the wellbore optimization problem a very challenging work. Several types of well are listed as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reach wells etcetera. Over the recent few years, the number of unconventional wells including deviated wells, highly deviated wells are steadily increasing. Directional drilling has some advantages over vertical drilling though it is more expensive. In drilling engineering, the optimization of wellbore plays an important role, which can be optimized based on minimization of length, mud pressure, critical pressure, etc. Till today so many approaches and methods are used to optimize this wellbore trajectory. From those methods in this study, we have focused on metaheuristic approaches based on PSO (particle swarm optimization) which will be used to optimize wellbore trajectory. This reduction of the wellbore length helps in establishing cost-effective approaches that can be utilized to resolve a group of complex trajectory optimization challenges. For smooth and effective performance (i.e. quickly locating global optima while taking the shortest amount of computational time) we must identify flexible control parameters. Later this parameter can be effectively fixed to tune different algorithm. This research will propose a new neighborhood function with Particle swarm optimization(PSO) algorithm for minimizing the true measured depth (TMD). In this paper, the authors have proposed a particle swarm optimization with neighbourhood function to solve this problem. Later the authors will compare this method with conventional methods. © 2019 IEEE. |
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