基于改进蚁群算法的钻进参数优化
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摘要
在钻井过程中,为了使钻进过程达到最优的技术和经济指标,需要选择合理的钻进参数。针对单目标钻进参数优化的局限性和不足,通过分析钻进参数之间的相互关系,综合考虑多个目标(如机械钻速最大、钻头寿命最长及钻头比能最小)建立一定约束条件下的多目标优化模型,实现最优的钻压-转速配合。采用改进的蚁群算法进行钻进参数优化,在具体的钻井实例中进行仿真,并将仿真结果与其他经典优化算法的结果进行对比分析。实验结果进一步证明了该模型和算法的有效性和实用性,为蚁群算法在钻进参数优化研究中的应用提供了理论依据。
In well drilling process,reasonable drilling parameters should be selected in order to achieve optimal technical and economic indexes.Aiming at the limitation and deficiencies of single-target drilling parameter optimization,a multi-objective optimization model is established under certain constraint through analyzing the interrelation among drilling parameters and comprehensively taking into account several targets(such as make drill speed maximize,make bit life maximize and make the specific energy of bit minimize) to obtain optimum BW-RPM matching.Drilling parameters are optimized with improved ant colony algorithm.A drilling caseis simulated,and compared with other classical optimization algorithms,the improved ant colony algorithm can more quickly obtain optimal BW-RPM matching.The experimental results further prove the validity and practicability of the model and algorithm,which provides a theoretical basis for the application of the ant colony algorithm in the optimization of drilling parameters.
In well drilling process,reasonable drilling parameters should be selected in order to achieve optimal technical and economic indexes.Aiming at the limitation and deficiencies of single-target drilling parameter optimization,a multi-objective optimization model is established under certain constraint through analyzing the interrelation among drilling parameters and comprehensively taking into account several targets(such as make drill speed maximize,make bit life maximize and make the specific energy of bit minimize) to obtain optimum BW-RPM matching.Drilling parameters are optimized with improved ant colony algorithm.A drilling caseis simulated,and compared with other classical optimization algorithms,the improved ant colony algorithm can more quickly obtain optimal BW-RPM matching.The experimental results further prove the validity and practicability of the model and algorithm,which provides a theoretical basis for the application of the ant colony algorithm in the optimization of drilling parameters.
引文
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[2] 李琳,聂臻,沙林秀.基于NSGA-Ⅱ的钻进参数多目标优化[J].石油机械,2013,41(3):1-4.LI Lin,NIE Zhen,SHA Linxiu.Multi-objective optimization based on the NSGA-Ⅱ drilling[J].Journal of Petroleum Machinery,2013,41(3):1-4.
[3] 景宁,蒋璐朦,耿智,等.基于改进遗传算法的钻井参数优选方法研究[J].石油机械,2016,44(1):1-6.JING Ning,JIANG Lumeng,GENG Zhi,et al.Research on drilling parameter optimization method based on improved genetic algorithm[J].Petroleum Machinery,2016,44(1):1-6.
[4] 沙林秀.基于快速自适应量子遗传算法的钻井参数优化[J].石油机械,2013,41(2):1-5.SHA Linxiu.Drilling parameter optimization based on fast adaptive quantum genetic algorithm[J].Petroleum Machinery,2013,41(2):1-5.
[5] 伊鹏,刘衍聪,郭欣,等.基于改进自适应遗传算法的钻井参数优化设计[J].石油机械,2010,38(2):1-5.YI Peng,LIU Yancong,GUO Xin,et al.Optimization design of drilling parameters based on improved adaptive genetic algorithm[J].Petroleum Machinery,2010,38(2):1-5.
[6] 张奇志,何素素,韩振华.钻进参数优化研究综述[J].石油化工应用,2015,34(2):1-5.ZHANG Qizhi,HE Susu,HAN Zhenhua.Research review on drilling parameter optimization[J].Petrochemical Applications,2015,34(2):1-5.
[7] 王凯,王荣鹏,刘宇,等.基于Pareto最优原理的钻机钻进参数多目标优化[J].中国机械工程,2017,28(13):1-8.WANG Kai,WANG Rongpeng,LIU Yu,et al.Multi-objective optimization of drilling parameters based on Pareto optimal principle[J].China Mechanical Engineering,2017,28(13):1-8.
[8] 周建成,覃宇.基于惩罚函数的双目标钻进参数优化方法[J].河南科技,2017,615(7):1-3.ZHOU Jiancheng,QIN Yu.Dual-objective drilling parameter optimization method based on penalty function[J].Henan Science and Technology,2017,615(7):1-3.
[9] VISERAS A,LOSADA R O,MERINO L.Planning with ants:efficient path planning with rapidly exploring random trees and ant colony optimization[J].International Journal of Advanced Robotic Systems,2016,13(5):1-16.
[10] JUANG C F,YEH Y T.Multiobjective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks[J].IEEE Transactions on Cybernetics,2017,30(99):1910-1922.
[11] LIU J H,YANG J G,LIU H P,et al.An improved ant colony algorithm for robot path planning[J].Soft Computing,2017,21(19):5829-5839.
[12] CAO J G.Robot global path planning based on an improved ant colony algorithm[J].Journal of Computer and Communications,2016,4(2):11-19.
[13] CHE H L,WU Z Z,KANG R X,et al.Global path planning for explosion-proof robot based on improved ant colony optimization[C].Asia-Pacific Conference on Intelligent Robot Systems.Tokyo,2016:36-40.
[14] HU Y S,LI D,DING Y.A path planning algorithm based on genetic and ant colony dynamic integration[C].Proceeding of the 11th World Congress on Intelligent Control and Automation,Shenyang,2014:4881-4886.
[15] CRISTIAN M,ELINA P,CARLOS G G.An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments[J].Advances in Engineering Software,2013,56:38-50.
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