In surface directional drilling, the rational and precise design of wellbore trajectories is essential. Conventional directional wellbore trajectories suffer from issues such as high frictional resistance and torque, poor stability, and trajectory control uncertainties. To address these challenges, this study proposes an optimized trajectory model for short-radius branched directional deflection wells, aiming to minimize the total wellbore length. First, a single-objective nonlinear constrained optimization problem is formulated, incorporating constraints on depth increment, inclination angle, and azimuth range. Then, to solve this optimization problem efficiently, a hybrid method combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is developed, enabling optimal trajectory design under the shortest-length constraint. The proposed method combines the rapid solving capability of particle swarm optimization with the comprehensive nonlinear solving capability of genetic algorithms. Leveraging high-performance flexible drilling tools, a wellhole trajectory control mechanism centered on thrust regulation is investigated. Finally, simulations are conducted using ANSYS. The simulation results indicate that the optimized trajectory significantly reduces the stress concentration effects in the formation, with a 58% decrease in the peak stress, and reduces the trajectory length. When applied to roof drilling at a working face in Caojiatan Mine, Shaanxi, the optimized drilling method reduced the drill tool wear by 35%, decreased the maximum thrust difference from 2.7 to 0.8 MPa, and improved the work efficiency (hole completion time) by 14% compared to conventional drilling. The results validate the enhanced stability and reduced wear characteristics of the proposed approach, offering significant theoretical and practical insights for the construction and design of short-vertical-distance drilling operations.
Yang et al. (Tue,) studied this question.