Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms

Wellbore trajectory design is a determinant issue in drilling engineering. This paper introduces a new stochastic approach for drilling trajectory design applying continuous particle swarm algorithms to find the optimum drilling measured depth of directional and horizontal wells in 3-D space. Considering all the constraints and limitations, the final goal is to determine all geometrical well parameters, in order to achieve the optimum measured depth to the desired target location. Particle swarm optimization is a computational algorithm inspired from natural behavior of some animal societies, such as flocks of birds and shoals of fish. In this review, the trajectory design method for an objective function, originally proposed by Adams and Charrier (1985), is explored and developed. Also, the attributes of the particle swarm optimization (e.g., Onwunalu, 2010) and Meta-optimization (e.g., Pedersen, 2010a,b) algorithms are considered and compared. These algorithms are then applied to demonstrate the determination of true measured depth of example horizontal wellbores as the objective function. Faster convergences, better final points which satisfy all constraints imposed on the drilling paths and population diversity maintenance to help the algorithms find better solutions, are positive characteristics of the solutions found using the algorithms proposed. These algorithms make promising new tools for designing economically-effective trajectories for deviated wells. codes for the PSO algorithms evaluated are provided as appendices to this article.