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水平井钻井提速-减阻-清屑多目标协同优化方法 被引量:5
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作者 丁建新 李雪松 +4 位作者 宋先知 张诚恺 马宝东 刘子豪 祝兆鹏 《石油机械》 北大核心 2023年第11期1-10,共10页
水平井钻井过程中,钻进破岩、管柱延伸和井筒流动等子系统相互耦合、相互制约,现有的仅考虑单一子系统的钻井优化方法难以兼顾效率和风险。针对此问题,提出了综合考虑破岩提速、管柱减阻和井筒清洁多过程耦合的水平井钻井提速-减阻-清... 水平井钻井过程中,钻进破岩、管柱延伸和井筒流动等子系统相互耦合、相互制约,现有的仅考虑单一子系统的钻井优化方法难以兼顾效率和风险。针对此问题,提出了综合考虑破岩提速、管柱减阻和井筒清洁多过程耦合的水平井钻井提速-减阻-清屑多目标协同优化方法,建立了基于随机森林的机械钻速智能预测模型、基于刚杆模型的摩阻系数智能反演模型和基于两层稳态模型的水平井岩屑运移模型,并以钻压、转速和排量为决策变量,以提高机械钻速、降低机械比能为目标,以不发生管柱风险(摩阻系数过大)和井眼清洁风险(岩屑床高度过高)为约束条件,采用带精英策略的非支配排序遗传算法和逼近理想解排序算法进行多目标优化与决策,给出钻压、转速和排量的最优参数组合。将该方法应用于某水平井7600~7650 m水平段进行实例分析。分析结果表明,优化后机械钻速提高了32%,机械比能降低了19%,且摩阻系数和岩屑床高度均在安全范围内。提出的水平井钻井提速-减阻-清屑多目标协同优化方法能够在优化钻井效率的同时降低卡钻和井眼清洁不足等风险。该方法可为水平井安全高效钻进提供保障。 展开更多
关键词 钻井优化 提速-减阻-清屑 多过程协同 多目标优化 机器学习
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Cooperative driving model for non-signalized intersections with cooperative games 被引量:8
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作者 YANG Zhuo HUANG He +2 位作者 WANG Guan PEI Xin YAO Dan-ya 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第9期2164-2181,共18页
Cooperative driving around intersections has aroused increasing interest in the last five years.Meanwhile,driving safety in non-signalized intersections has become an issue that has attracted attention globally.In vie... Cooperative driving around intersections has aroused increasing interest in the last five years.Meanwhile,driving safety in non-signalized intersections has become an issue that has attracted attention globally.In view of the potential collision risk when more than three vehicles approach a non-signalized intersection from different directions,we propose a driving model using cooperative game theory.First,the characteristic functions of this model are primarily established on each vehicle’s profit function and include safety,rapidity and comfort indicators.Second,the Shapley theorem is adopted,and its group rationality,individual rationality,and uniqueness are proved to be suitable for the characteristic functions of the model.Following this,different drivers’characteristics are considered.In order to simplify the calculation process,a zero-mean normalization method is introduced.In addition,a genetic algorithm method is adopted to search an optimal strategy set in the constrained multi-objective optimization problem.Finally,the model is confirmed as valid after simulation with a series of initial conditions. 展开更多
关键词 cooperative driving multi-vehicles-cross process cooperative games Shapley value genetic algorithm
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