Meng Yuan

A methodology to restructure a pipeline system for an oilfield in the mid to late stages of development

Abstract One important issue in the mid to late development stages of oilfields is maintaining stable production, especially when the existing gathering pipeline system cannot fully satisfy the development of low pressures and low production rates. In these cases, it is necessary to restructure the original gathering pipeline system. In this study, an optimal design method is proposed to restructure a pipeline system in an oilfield in the mid to late development stages. Based on the demand of stable production and the existing structure of the pipeline system, a mixed-integer nonlinear programming (MINLP) model with an objective function that minimizes the total cost is developed. Hydraulic, technical and economic constraints are considered. The model is linearized based on a piecewise method and solved by the branch-and-bound algorithm. This method is applied to a real case study of a pipeline system in an oilfield.

A novel particle swarm optimization based on prey–predator relationship

Abstract Particle swarm optimization (PSO) is a widely used nature-inspired optimization algorithm based on population and has strong robustness and good global astringency. In the mid–late iterations in the latest PSOs, there are plenty of dense gathering “slothful particles” with low velocities, which not only contribute little to the optimization but also impact the computation speed. Inspired by the prey–predator relationship in nature, we propose a novel prey–predator PSO (PP-PSO) that employs the three strategies of catch, escape, and breeding. In PP-PSO, slothful particles can be deleted or transformed, and while the former helps to speed up convergence and computation speed, the latter improves optimization results. In addition, a proportional-integral (PI) control is introduced for population control, where the population fluctuates but within a relative stability over iteration, thus enhancing population diversity. The experimental study on 10 basic benchmark functions and 30 advanced benchmark functions from CEC 2017 with different dimensions shows that our PP-PSO has a superior performance in comparison with ten other peer algorithms. In this study, a novel relationship between species behavior and swarm intelligence algorithm is found and the mechanism of particle motion in the convergence process of PSO is further revealed.