Multi-objective cellular particle swarm optimization for wellbore trajectory design

Abstract

Wellbore trajectory design is a determinant issue in drilling engineering. The criteria to evaluate a wellbore trajectory are summarized as the total trajectory length, the torque and the well profile energy in this paper. By minimizing the wellbore trajectory length, torque and profile energy simultaneously, it is most likely that a wellbore trajectory designed to arrive at the specific target can be drilled more safely, quickly and cheaply than other potential trajectories. However, these three objectives are often in conflict with each other and related in a highly nonlinear relationship. A multi-objective cellular particle swarm optimization (MOCPSO) with an adaptive neighborhood function is developed in this paper. Then, MOCPSO is applied with the three objective functions to gain a set of Pareto optimal solutions that are beneficial for a less risky and less costly wellbore trajectory design option. Besides, MOCPSO’s performance is compared with multi-objective PSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm-II (NSGA-II). Effect of the proposed neighborhood function is also investigated by making contrasts with the commonly used four neighborhood templates. Moreover, the radius parameter in the adaptive neighborhood function is analyzed to reveal its influence on the optimization performance. It can be inferred that MOCPSO is statistically superior to both multi-objective PSO, NSGA-II and MOEA/D at the 0.05 level of significance on the wellbore trajectory design problem. And the proposed adaptive neighborhood function performs either comparable or better as compared to the other commonly used neighborhood functions. According to the parameter analysis, it can be concluded that the MOCPSO approach with radius value of 1or 1.5 has a better statistical performance.