基于粒子群算法的井眼轨迹优化研究

为了更优更快地对石油工程中的井眼轨迹进行优化,进行了基于改进粒子群优化(PSO)算法的井眼轨迹优化研究.通过对造斜率归一化,推导出目标函数表达式,将问题归结到对式中参数优化问题上来.引入PSO算法,在保持了PSO算法结构简单可行特点的同时,利用惩罚函数方法和叉乘控制项,对基本PSO算法易限入局部极小点周边区域的局限进行了改进.该井眼轨迹模型和相应算法提高了井眼轨迹优化速度.通过对钻井工程中轨迹参数的优化实践,验证了本算法优于基本的PSO算法,较好地实现了对井眼轨迹的优化.

Research on optimization of well track based on PSO algorithm

For the purpose of optimization of well track in petroleum engineering, the well track optimization based on particle swarm optimization (PSO) algorithm has been studied. By making the rate of making bevel as initial known parameter, the target function expression has been concluded. Then the main problem is the optimization of parameters of function. Meanwhile the PSO algorithm is introduced with keeping the merits of briefness and easy realization, by adopting the method of penalty function and the forking product controlling item, and the shortcoming of the original algorithm for getting into the scope around local particle point is overcome. This module and corresponding algorithms have improved the efficiency of the optimization of well track. By the operation in engineering practice, the example validates that the improved algorithm is better than the original PSO algorithm.