# Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation
## 1 Introduction
[PERSON] and [PERSON] [7] (2022) realized the optimization of wellbore trajectory based on multi-objective particle swarm optimization algorithm to mutate particles. In the study of wellbore trajectory optimization, [PERSON] [8] et al. (2023) proposed a novel adaptive grid multi-objective particle swarm optimization algorithm (AGMOPSO) to obtain a set of Pareto optimal solutions of the established optimization model. Ant colony optimization (ACO) is also a typical heuristic search optimization algorithm, which was first proposed and widely accepted by Italian scholar [PERSON] [9] in his 1996 paper \"Ant System: Optimization by A Colony of Conforming Agents\". In order to further improve the iteration efficiency and solution accuracy of ant colony algorithm, [PERSON] [10] (2011) and [PERSON] [11] (2012) successively improved the ant colony algorithm, and proposed an ant colony hybrid algorithm based on chaotic technology and an improved ant colony algorithm based on the adaptive pheromone volatilization factor. Although their research has improved the iteration efficiency of the ant colony algorithm, it is still unable to solve the huge challenges in wellbore trajectory planning. [PERSON] and [PERSON] [12] (2015) introduced the basic principle and application scope of the ant colony algorithm in detail in their published articles. In view of this, [PERSON] [13] proposed a wellbore trajectory planning research method based on the ant colony algorithm. Firstly, the sum of squares of the increment difference of wellbore trajectory coordinates is taken as the constraint condition of the objective function and azimuth range, then the continuous space is processed by grid, and finally the ant colony algorithm is used to solve the problem. Although [PERSON]'s research shows that the ant colony algorithm has fast convergence speed and good solution accuracy in wellbore trajectory planning, the computational efficiency of the ant colony algorithm will be greatly reduced in the face of more complex construction scenarios. [PERSON] [14] (2001) analyzed the characteristics and disadvantages of common methods in the design and construction of non-straight well traces, and established a multi-constraint optimization model called CORA. According to the investigation, this was the first attempt to comprehensively and quantitatively introduce factors affecting the design and control of non-vertical well traces into the model. In the work of [PERSON] [5] (2004), factors such as the selection range of the build-up point in the actual formation and the wellhead positioning were also incorporated into the model, which further improved the rationality and reliability of the model. [PERSON] [15] (2013) took Shengli Oilfield as a case study. In view of the formation problems of the Yan 227 block, such as the high risk of rockfalls and collapse, and the low requirement of the horizontal section wellbore expansion rate in its optimal design scheme, it is proposed that not only the dogleg degree should be reduced, but also the need of anti-collision wells should be fully considered to meet the requirements of safe construction. [PERSON] [16] (2014) and [PERSON] and [PERSON] [17] (2021) have carried out in-depth research on oilfield drilling in Sichuan area. [PERSON] proposed that shale gas resources in southern Sichuan are very rich. However, due to the complex geological structure and stress of the reservoir and the development of shale bedding in this area, the wellbore stability of the horizontal section is poor, and it is prone to collapse and sticking. In order to solve this problem, [PERSON] established a collapse failure model of stratified formation to provide a reference for the selection of horizontal section orientation of horizontal wells in the work area. [PERSON] and [PERSON] et al.