An Intelligent Well-Path Design Approach Based on Probabilistic Roadmap Method for Drilling in Congested Environments

Abstract Well path planning in congested environments, such as well clusters with large number of existing wells, is both challenging and time consuming due to anticollision and other engineering constraints. In this paper, we propose an intelligent well-path design approach by combining probabilistic roadmap method (PRM), a sampling-based motion planning technique, with traditional well trajectory algorithms for well planning in congested environments. PRM is adapted to well-trajectory design applications by defining suitable configuration space, collision detection, sampling, and path optimization methods. The modified PRM constructs a roadmap of feasible paths in given configuration space and then queries the roadmap for optimal paths. Engineering constraints checking are performed for the optimal paths and a smoothing algorithm is employed to tune the unfeasible local curves. The proposed approach is applied to a 2D case and a 3D case to illustrate its practicability for well-trajectory design in congested environments. Results show that it can generate feasible and optimal well trajectories automatically for congested environments while satisfying collision and other constraints, which could reduce the well design efforts significantly.