Chemistry and Technology of Fuels and Oils

, Volume 55, Issue 2, pp 213–218 | Cite as

Application of the Artificial Fish Swarm Algorithm to Well Trajectory Optimization

  • Tengfei Sun
  • Hui Zhang
  • Deli Gao
  • Shujie Liu
  • Yanfeng Cao

Drilling applications involve a number of global optimization problems that require finding the best extremum value of a nonlinear function of many variables. One of such problems is the choice of the optimal well drilling trajectory. Various trajectory optimization algorithms have been previously proposed, but they all suffer from some shortcomings. In the present paper, the shortest well length is used as the objective function, and optimization is performed by the artificial fish swarm algorithm (AFSA). The calculations have been carried out in the Matlab environment. Comparison of our calculations with previously published data suggests that AFSA optimization produces the best numerical results and the shortest trajectory, while in addition ensuring high stability and reliability. The algorithm has a simple structure and fast convergence, quickly producing a global optimum. AFSA thus may be used to calculate the optimal drilling trajectory.


artificial fish swarm algorithm well length drilling trajectory optimization. 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tengfei Sun
    • 1
    • 2
  • Hui Zhang
    • 3
  • Deli Gao
    • 3
  • Shujie Liu
    • 2
  • Yanfeng Cao
    • 2
  1. 1.Beijing University of Chemical TechnologyBeijingChina
  2. 2.CNOOC Research InstituteBeijingChina
  3. 3.Department of Petroleum EngineeringChina University of PetroleumBeijingChina

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