Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism

Borehole trajectory planning under complex geological conditions poses significant challenges for intelligent drilling systems. To tackle this issue, a novel optimization framework is developed, leveraging the Soft Actor-Critic (SAC) algorithm enhanced by a self-attention mechanism. A three-dimensional heterogeneous geological model is constructed via generative adversarial networks (GANs), incorporating key formation features such as lithology, pressure, and fault zones. A tailored multi-objective reward function is introduced, balancing directional convergence, trajectory smoothness, obstacle avoidance, and formation adaptability. The self-attention mechanism is embedded into both the actor and critic networks to strengthen the agent’s capacity for spatial perception and decision stability. The proposed approach enables the agent to adaptively generate control sequences for efficient trajectory planning in highly variable formations. Experimental results demonstrate that the model exhibits superior convergence stability, improved curvature control, and enhanced obstacle avoidance, highlighting its potential for intelligent trajectory planning in challenging drilling environments.