地形复杂气田多级星状集输管网拓扑优化

Topology optimization of multi-level star-shaped gathering pipeline network in topographically complicated gas fields

气田集输系统为多级网络结构,通常采用分级优化的方式进行设计,且在计算过程中忽略地形起伏,将障碍物区域简化为平面凸多边形,其计算结果与工程实际相差较大。为了优化设计方案,以管网总长度最短为目标函数,将不同级别站点间的隶属关系、处理量、空间位置限制作为约束条件,建立了多级星状集输管网整体优化模型。在模型中引入三维地形和障碍因素,包含大量离散变量和连续变量,且约束条件呈非线性。为提高全局寻优能力,采用蚁群算法和粒子群算法相结合的群智能技术求解,为复杂地形的管网拓扑优化提供了高效准确的方法。算例计算结果表明:地形起伏和障碍影响管道走向、站点位置、管网长度,对真实地表特征进行模拟,实现三维曲面上的最优避障路径规划,基于三维地形和障碍的集输管网长度增幅约20%。

The gathering system in a gas field is a multi-level network structure and its design is usually in the mode of hierarchical optimization. In the process of its calculation, the topographic relief is ignored and the obstacles are usually simplified into convex polygons, so the calculation result is more different from the practical engineering. In order to optimize the design scheme, the overall optimization model of multi-level star-shaped gathering pipeline network was established with the shortest total length of the pipeline network as the objective function and the affiliation between the stations of different levels, the processing capacity and the spatial position limitation as the constraints. In this model, threedimensional terrain and obstacle factors are introduced, including a large number of discrete variables and continuous variables, and the constraints are nonlinear. In order to improve the global optimization ability, the group intelligence technology which combines ant colony algorithm and particle swarm algorithm together is used to obtain the solution, so as to provide an efficient and accurate method for the topology optimization of pipeline network in complicated terrains. The example calculation results show that three-dimensional terrain and obstacles affect the pipeline orientation, station position and pipeline network length. The actual surface characteristics can be simulated and the optimal obstacle avoidance path can be planned on the three-dimensional camber. The length of the gathering pipeline network based on three-dimensional terrain and obstacles is increased by about 20%.