Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app15168788

Authors: Xiaowei Li, Haipeng Gu, Yang Wu, Zhaokai Hou

Institution: Northeast Petroleum University

Language: English

Topic: Drilling and Well Engineering

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Northeast Petroleum University