Application of the Artificial Fish Swarm Algorithm to Well Trajectory Optimization
- 作者: Sun T.1,2, Zhang H.3, Gao D.3, Liu S.2, Cao Y.2
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隶属关系:
- Beijing University of Chemical Technology
- CNOOC Research Institute
- Department of Petroleum Engineering, China University of Petroleum
- 期: 卷 55, 编号 2 (2019)
- 页面: 213-218
- 栏目: Article
- URL: https://journals.rcsi.science/0009-3092/article/view/235823
- DOI: https://doi.org/10.1007/s10553-019-01023-7
- ID: 235823
如何引用文章
详细
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.
作者简介
Tengfei Sun
Beijing University of Chemical Technology; CNOOC Research Institute
Email: bortum@mail.ru
中国, Beijing, 100029; Beijing
Hui Zhang
Department of Petroleum Engineering, China University of Petroleum
Email: bortum@mail.ru
中国, Beijing
Deli Gao
Department of Petroleum Engineering, China University of Petroleum
Email: bortum@mail.ru
中国, Beijing
Shujie Liu
CNOOC Research Institute
Email: bortum@mail.ru
中国, Beijing
Yanfeng Cao
CNOOC Research Institute
Email: bortum@mail.ru
中国, Beijing