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作 者:任旭虎[1] 王文倩 刘通 闫宇晴 吴鹏宇 Ren Xuhu;Wang Wenqian;Liu Tong;Yan Yuqing;Wu Pengyu(College of Oceanography and Space Informatics,China University of Petroleum(East China))
机构地区:[1]中国石油大学(华东)海洋与空间信息学院,山东省青岛市266580
出 处:《石油机械》2025年第3期1-9,共9页China Petroleum Machinery
基 金:中石化经纬有限公司-中国石油大学(华东)联合开发项目“井筒液位测量实验测试”(HX20220489)。
摘 要:针对钻井作业中复杂噪声干扰了钻井返出液回波信号的准确提取和旅行时间计算、降低了钻井返出液液面检测精度的问题,提出了一种基于粒子群(Particle Swarm Optimization,PSO)的随机共振钻井返出液信号处理算法。通过引入非线性惯性权重及新的个体最优值,对标准PSO算法进行优化,克服其易陷入局部极值缺陷的问题,获得实现原始信号共振效应的最佳非线性项系数,采用4阶龙格-库塔算法对随机共振进行求解,从而获得最佳输出信号。对不同井况的钻井返出液信号进行仿真和测试。测试结果表明:基于PSO的随机共振方法在有效滤除噪声的同时保留了信号的原始特征;处理后的信号一致性良好,表现出平滑稳定的特性。该方法提高了钻井返出液液面检测的精度,可为钻井返出液检测信号的处理提供一种高效可行的方法。Complex noises in drilling operations interfere the accurate echo signal extraction and echo travel time calculation for returned drilling fluid,and reduce the accuracy of returned drilling fluid level detection.This paper presents a stochastic resonance method for returned drilling fluid signal processing based on particle swarm optimization(PSO).The standard PSO algorithm is optimized by introducing nonlinear inertia weights and new individual optimal values to address the defect that it may easily run into local extreme,so that the optimal nonlinear term coefficients enabling the resonance effect of original signals are obtained.Then,the stochastic resonance is solved by using the fourth-order Lunger-Kutta algorithm,so as to obtain the optimal output signals.The returned drilling fluid signals under different well conditions were simulated and tested.The results indicate that the PSObased stochastic resonance method effectively filters out the noise while retaining the original features of the signals.The processed signals are well consistent and show smooth and stable characteristics.The processed PSO-based stochastic resonance method improves the accuracy of returned drilling liquid level detection,and provides an efficient.
关 键 词:钻井作业 钻井返出液回波信号 回波旅行时间 随机共振 粒子群 数据采集处理
分 类 号:TE28[石油与天然气工程—油气井工程]
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