A new method for minimum distance separation factor calculation based on particle swarm optimization algorithm

Anti-collision technology for wellbores is integrated into both the pre-drilling design and real-time drilling monitoring of cluster wells, infill wells, and directional wells. This technology encompasses well separation distance, position uncertainty analysis, and the separation factor method. Because the separation factor method comprehensively accounts for the first two approaches, its evaluation results serve as a crucial foundation for wellbore anti-collision design and construction. The accuracy of a separation factor method depends on how the error ellipsoid's posture is handled during position uncertainty analysis. Existing methods—including the traditional separation factor method, central vector method, perpendicular line method, and oriented separation factor method—each exhibit accuracy limitations. The traditional separation factor method and the perpendicular line method tend to be overly conservative, whereas the central vector method and the oriented separation factor method often yield overly optimistic results. The minimum distance separation factor method proposed by the Industry Steering Committee on Wellbore Survey Accuracy (ISCWSA) provides a more precise assessment of wellbore separation, but its calculation model has not been explicitly published. Some researchers have attempted to address the minimum distance calculation problem for ellipsoids using constrained optimization theory. However, this approach faces challenges related to convergence and low computational efficiency. To overcome these issues, this paper proposes a minimum distance separation factor calculation method based on the particle swarm optimization algorithm. By iteratively determining the minimum distance between error ellipsoids in three-dimensional space, this method achieves higher computational efficiency and greater accuracy, making it particularly suitable for dense cluster well design and anti-collision analysis. Additionally, due to the simplicity and minimal parameter requirements of the particle swarm optimization algorithm, this approach is more practical for field applications.