# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 5 Conclusions  
In this paper, an intelligent optimization design method of wellbore trajectory combining a self-attention mechanism and Soft Actor-Critic (SAC) algorithm is proposed. Combined with the three-dimensional geological modeling technology constructed by WGAN, a reinforcement learning environment for trajectory design under complex geological conditions is systematically constructed. By introducing the attention mechanism, the strategy's ability to mine state history information is effectively enhanced, and the model's ability to identify and utilize key features in multi-source logging data is improved. The experimental results show that compared with the original SAC algorithm, the proposed method has significant advantages in convergence speed, obstacle avoidance ability, trajectory continuity, and strategy stability. For example, compared to the baseline SAC algorithm, the improved SAC + Attention model achieved a 25% faster convergence within the first 20,000 training steps, as shown in Figure 16. The average reward improved from \(-1.77\) to \(-1.76\), and the success rate of obstacle avoidance increased by 12.5% in dual-obstacle scenarios. These metrics demonstrate better learning efficiency, more stable path optimization, and enhanced applicability under complex geological constraints. Furthermore, the strategy fluctuation range (standard deviation) of the model in 500 consecutive iterations is 0.03, which is only 43% of that of the baseline model (0.07), demonstrating stronger strategy stability. These metrics further verify that the self-attention mechanism enhances the model's environmental perception and decision-making robustness. Especially in the face of various typical downhole interference scenarios, such as initial, middle, end, and double obstacles, the improved algorithm can achieve high-quality obstacle avoidance, ensure the construction feasibility and target accuracy of the trajectory, and verify the strong adaptability and practicability of the method. Although the proposed method shows strong performance in simulated environments, the current study does not include validation with real-world field data. This is due to the confidentiality and access restrictions associated with industrial drilling operations. In future work, we aim to collaborate with industry partners to deploy the method on field-scale datasets and assess its performance under real geological conditions. This step will be essential for advancing from simulation to field-level application. Training the SAC + Attention model takes approximately 4-6 h on a single RTX 4070 GPU. Once trained, the model is lightweight and can be deployed in real time on standard engineering workstations, making it practical for field application. Compared to traditional trajectory planning methods based on geometric heuristics or static optimization, the proposed approach demonstrates superior adaptability to dynamic geological changes, continuous decision-making capability, and data-driven obstacle avoidance. In contrast to prior reinforcement learning approaches such as DQN or DDPG, our SAC + Attention model offers better convergence stability and long-horizon decision awareness. These improvements make it particularly suitable for real-time intelligent drilling applications. In practical terms, the method's ability to generate smooth, feasible, and target-aligned trajectories in multi-obstacle scenarios supports its deployment in complex drilling environments such as faulted zones, high-pressure intervals, and high-density well fields. This highlights its potential for enhancing drilling safety, improving target precision, and reducing unplanned toolface adjustments in the field. Future research can further explore the hierarchical modeling ability of multi-scale attention structure in trajectory control tasks and combine advanced structures, such as a Transformer, to improve the robustness of the model to complex environmental changes.