# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 5 Conclusions  
Focusing on the trajectory planning problem in a complex downhole environment, this paper systematically constructs a reinforcement learning wellbore trajectory design method based on the TD3 algorithm; proposes a state space construction method covering the well deviation angle, the azimuth angle, and the spatial position; designs an action space that meets the requirements of engineering constructability; and integrates multi-dimensional factors, such as obstacle avoidance constraints, target entry accuracy, and trajectory smoothness, to construct a composite reward function. The constructed environment can simulate the control instruction mechanism in the real drilling process and guide the agent to learn the optimal drilling strategy through multiple interactions. The experimental results show that the method achieved good performance in three-stage trajectory construction, obstacle avoidance, autonomous adjustment, and target hitting. In the barrier-free case, the reinforcement learning strategy can achieve a stable and smooth three-stage trajectory design, where the curvature changes continuously, and finally can accurately hit the target. In the obstacle interference environment, the agent exhibits excellent path avoidance ability and direction fine-tuning ability and can actively adjust the trajectory shape according to different obstacle distributions to achieve the balance between space obstacle avoidance and target docking. In the multi-obstacle coupling environment, the reinforcement learning strategy still has the ability to generate feasible paths, indicating that it has strong generalization and adaptability to complex drilling scenarios. Specifically, TD3 reduces the well deviation error by 35% (from 1.2\({}^{\circ}\) to 0.78\({}^{\circ}\)) and the KOP error by 28% (5.6 m to 4.0 m) compared to multi-objective algorithms. Field applications demonstrate 40% shorter planning time and 12.7% cost reduction per well. Compared with multi-objective optimization algorithms, TD3 reduces errors in well deviation and KOP optimization, with its dual Q network demonstrating superior robustness in complex formations. This technology is directly applicable to shale gas horizontal wells and deepwater extended reach wells, as validated by field cases like Shengli Oilfield. Key modeling assumptions and limitations should be noted:  
**Step 1:** Fixed 0.5 m step size (actual step size varies with formation hardness);  
**Step 2:** Noise-free sensor observations (field measurements have \(\pm\)0.3\({}^{\circ}\) error, as discussed in Paragraphs 1-121);  
**Step 3:** Static formation parameters (neglecting pressure depletion during drilling). These limitations motivate future work on adaptive step sizing and Kalman filtering for noise reduction. First, the reinforcement learning model has a certain sensitivity to the initial environment setting, and the generalization performance needs to be further improved by environment normalization and dynamic parameter adjustment. Second, the design of the reward function still depends on engineering experience in weight value and target balance. In the future, it can be further optimized by combining fuzzy reasoning, hierarchical learning, and other mechanisms. In summary, this study provides a theoretical and practical basis for constructing an intelligent wellbore trajectory planning system that adapts to complex downhole environments. The introduction of reinforcement learning methods not only breaks through the static modeling limitations of traditional optimization methods but also supports real-time field deployment through (1) integration with LWD/MWD sensors for 1 Hz data acquisition; (2) edge computing architecture to reduce latency to \(<\)50 ms; and (3) physical constraint boundaries (e.g., max build rate) as fail-safes. These solutions address the challenges in real-time data acquisition and communication latency highlighted in the review. **Author Contributions:** Methodology and validation, H.G., X.L., and Y.W. ; survey and resources, H.G.