摘要
为了使钻进过程达到最优,提出了基于机械钻速、钻头寿命和钻头比能的钻进参数多目标优化模型。参考典型的多目标优化进化算法NSGA-Ⅱ,提出了一种多目标粒子群算法(MOPSO)。采用一个钻进参数优化实例对优化模型和算法进行检验,得到分布均匀的Pareto最优解,一些最优解与传统的钻进参数单目标优化的解近似;讨论了算法中的种群规模、迭代次数和外部档案规模三个参数,得到一组兼顾解质量和计算时间的参数值,其计算时间的统计结果证明模型和算法满足钻进参数动态优化的要求。
In order to make the drilling process optimal,a multi-objective optimization model of drilling parameters based on the penetration rate,the bit life and the mechanical specific energy of bit is proposed. Inspired by a classic multi-objective evolution optimization algorithm NSGA-II,a multi-objective particle swarm optimization （MOPSO） algorithm is proposed. The optimized model and algorithm were verified with a real example whose drilling parameters were optimized. The evenly distrib-uted Pareto optimal solutions were obtained,some of which are similar to the solutions of the traditional single-objective optimi-zation of drilling parameter. Three parameters of population scale,iteration number and external population scale in the algo-rithm are discussed. A group of parameter values which balance the solution quality and the computing time are chosen. The sta-tistical result of computing time proves that the model and the algorithm meet the need of the dynamic optimization of drilling pa-rameters.
出处
《现代电子技术》
2014年第10期24-27,共4页
Modern Electronics Technique
基金
陕西省自然基金:基于随钻测量地层识别的智能钻参优化方法的研究(2012JQ8046)
