# Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation
## 6 Conclusions  
In this paper, a multi-objective particle swarm optimization algorithm (MOPSO) combining clustering strategy and elite learning strategy is proposed for the three-stage wellbore trajectory optimization problem. By constructing a multi-objective optimization model aiming at wellbore trajectory length and trajectory complexity, the balance between economy and operability in wellbore trajectory design was realized. Experimental results showed that the improved algorithm is superior to the traditional method in the performance of classical benchmark functions, and shows significant advantages in the diversity and convergence of solutions. The improved algorithm was applied to the actual wellbore trajectory optimization problem, and satisfactory optimization results were obtained. The optimized wellbore trajectory significantly shortens the trajectory length and reduces the trajectory complexity, which provides an efficient solution for trajectory design under complex geological conditions. The models and methods proposed in this study not only improve the design efficiency of wellbore trajectory, but also have good engineering adaptability and promotion potential. In summary, this study provides new theoretical and methodological support for wellbore trajectory optimization. Future research can further combine the dynamic adjustment of wellbore trajectory, performance optimization of drilling tools and intelligent control technology to expand the practical application scenarios of the algorithm, so as to promote the further development of wellbore trajectory optimization technology.  
The computational resources required for the proposed algorithm were evaluated on a standard engineering workstation with an Intel i7 processor and 16 GB RAM. The average runtime for a single optimization was 15 min, demonstrating industrial feasibility. However, scaling the algorithm for larger datasets may require distributed computing frameworks, which could be explored in future research.  
Methodology validation, H.G., T.Y. and Y.W.; survey resources, H.G.; data monitoring, T.Y.; writing--first draft preparation, Y.W.; writing--review and editing, H.G.; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52274005) and the Guiding Innovation Fund of Northeast Petroleum University: Intelligent Optimization Design of Horizontal Well Trajectory (Grant No. 2022 YDL-16).  
As the study does not involve human subjects or animal experiments, the requirement for a traditional ethical review by an Institutional Review Board is not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are not publicly available due to confidentiality and security restrictions. For further inquiries, please contact the corresponding author.  
The authors would like to thank Northeast Petroleum University for providing technical and administrative support during this research. The authors also appreciate the constructive feedback provided by colleagues, which greatly improved the quality of this study.  
The authors declare no conflict of interest.