WO2020078118A1 - 应用经验模态分解和粒子群优化算法的泵噪消除方法 - Google Patents

应用经验模态分解和粒子群优化算法的泵噪消除方法 Download PDF

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WO2020078118A1
WO2020078118A1 PCT/CN2019/103602 CN2019103602W WO2020078118A1 WO 2020078118 A1 WO2020078118 A1 WO 2020078118A1 CN 2019103602 W CN2019103602 W CN 2019103602W WO 2020078118 A1 WO2020078118 A1 WO 2020078118A1
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pump noise
particle swarm
pump
waveform
noise
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French (fr)
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瞿逢重
江琴
张昱森
靳国正
张璟辰
张祝军
吴叶舟
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浙江大学
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Priority to JP2020513792A priority Critical patent/JP6878690B2/ja
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Priority to US17/232,162 priority patent/US20210231487A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H3/00—Measuring characteristics of vibrations by using a detector in a fluid
    • G01H3/10—Amplitude; Power
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H3/00—Measuring characteristics of vibrations by using a detector in a fluid
    • G01H3/04—Frequency
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/12—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
    • E21B47/14—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling using acoustic waves
    • E21B47/18—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling using acoustic waves through the well fluid, e.g. mud pressure pulse telemetry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising

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  • the invention belongs to the technical field of wireless logging while drilling, and relates to a pump noise elimination method using empirical mode decomposition (EMD) and particle swarm optimization algorithm (Particle Swarm Optimization, PSO).
  • EMD empirical mode decomposition
  • PSO particle swarm optimization algorithm
  • Mud pulse is the pressure wave signal converted by the mud pump under the action of the mud pump after the data measured by the downhole instrument is converted into the electrical signal, and finally the mud is used as the medium to transmit the signal to the surface. Its reliability is high, the transmission distance is long, and it is more in line with the actual situation of drilling. It is a common transmission method in China. Because the mud pump piston needs to continuously reciprocate during the mud transmission signal process, and periodic pump noise will be generated during the movement, so for the mud pulse signal, the pump noise must be eliminated before the signal can be correctly decoded.
  • the mud pulse communication system is a time-varying system.
  • the periodic hypothesis of pump noise is an approximation hypothesis established within a time window of finite length. As the operating time of the system increases, the difference between the acquired pump noise samples and the waveform of the pump noise within a unit period will gradually increase, resulting in an increase in the residual noise in the noise canceling output, affecting the noise canceling effect.
  • the purpose of the present invention is to provide a pump noise elimination method using empirical mode decomposition and particle swarm optimization algorithm in view of the shortcomings of the prior art. Better pump noise elimination effect.
  • the object of the present invention is achieved by the following technical solution: a pump noise elimination method using empirical mode decomposition and particle swarm optimization algorithm, characterized in that the method includes the following steps:
  • the mud pressure signal of step 1 is intercepted in segments with the period T of step 2 as a time interval, and all the segmented signals are summed and averaged; the average value is closest to the actual periodic pump noise in a single cycle
  • the step 5 is specifically: for the particle swarm optimization algorithm, the initialization weight coefficient is 1, and the PSO parameters are initialized, and the PSO parameters include the upper and lower bounds of the weight coefficient, the number of particles, and the maximum number of iterations. Then iterate the decoding process.
  • the received signal minus the empirical waveform of the pump noise is decoded for equalization decision, and its mean square error value (Mean Square Value, MSE) is calculated as the output feedback parameter.
  • MSE mean square error value
  • the updated weight coefficient and the corresponding The bases of are multiplied, and then all the products are added to get the updated empirical waveform.
  • MSE as a cost function for the next iteration, until the maximum number of iterations or iteration termination conditions are reached, the final weight coefficient is multiplied by the corresponding basis, and the pump noise is removed from the received signal.
  • the calculation method of MSE is as follows:
  • w is the weight coefficient vector of each base
  • N is the number of symbols for this denoising
  • d i is the decision value of the i-th symbol
  • the physical meaning of MSE is the error power (Error Power) of the decoding output.
  • the particle swarm algorithm judges the direction of the particle's progress through the change trend of MSE, so as to find the best weight coefficient and improve the noise reduction effect.
  • the beneficial effect of the present invention is that the basic idea of the pump noise elimination method using EMD and PSO in the present invention is to treat the pump noise as a linear combination of a group of bases, and the update process of the pump noise is to determine the most of this group of bases based on the decision output Best linear combination.
  • EMD decomposes the pump noise samples into a set of bases, which can be used to reconstruct a waveform estimate that is closer to the actual pump noise.
  • PSO can find the coefficient of the best linear combination of this group of bases as an update mechanism for the pump noise samples.
  • the present invention corrects the current pump noise samples in a weighted form in a limited number of denoising cycles, so that it gradually converges to the changed pump noise waveform in a unit cycle within a limited number of iterations to adapt the system to a long time
  • the pump noise changes slowly during operation.
  • Figure 1 is a block diagram of the pump noise cancellation method based on EMD-PSO
  • Figure 2 is a schematic diagram of the sensor pressure signal
  • Figure 3 is a schematic diagram of the pumping signal
  • Figure 4 is a schematic diagram of pump noise samples obtained by coherent averaging
  • Fig. 5 is a waveform diagram of each channel signal obtained by decomposing pump impulsive samples using EMD;
  • Figure 6 is a schematic diagram of the output signal of noise reduction
  • FIG. 7 is a schematic diagram of the amplification of the noise-cancelling output signal.
  • Figure 1 is a structural diagram of the pump noise cancellation method based on EMD-PSO.
  • the pressure signal measured by the downhole sensor we will sequentially perform a low-pass filter, use the coherent average method to extract the empirical waveform of the pump noise, and then Use the EMD-PSO joint iterative method to update the pump noise samples until they match the actual pump noise waveform.
  • a section of real-well dual-pump data is selected as the pressure signal.
  • the waveform is shown in Figure 2.
  • the basic frequencies of the dual pumps are 0.994Hz and 1Hz respectively, the modulation mode is FSK, the code rate is 13bps, and the depth is 2890m.
  • the performance index of the low-pass filter After obtaining the measured pressure signal from the sensor, first, determine the performance index of the low-pass filter according to the characteristics of the pressure data and perform low-pass filtering to obtain a mud pressure signal that filters out some white noise;
  • the pumping signal is measured by the pumping sensor.
  • the pump flushing sensor is a displacement sensor or a travel switch installed on the mud pump, which is used to record the position information of the mud pump piston.
  • the stroke switch type pumping sensor as an example, its output is generally a set of switching sequence composed of rectangular pulse signals. A low level indicates that the travel switch has not been triggered, and a high level indicates that the travel switch has been triggered. The rising edge of each rectangular pulse corresponds to the moment when the piston reaches the travel switch.
  • the pressure signal is segmented and intercepted at a time interval of T, and all the segmented signals are summed and averaged.
  • the average value obtained is closest to the empirical waveform of the actual waveform of the periodic pump noise in a single period, that is, the pump noise sample, as shown in Figure 4;
  • the weight coefficient For the particle swarm optimization algorithm, initialize the weight coefficient to 1, initialize the PSO parameters, such as the upper and lower bounds of the weight coefficient, the number of particles, and the maximum number of iterations, and then start the iteration of the decoding process.
  • the received signal minus the empirical waveform of the pump noise is decoded for equalization decision, and its mean square error value (Mean Square Value, MSE) is calculated as the output feedback parameter.
  • MSE mean square error value
  • the updated weight coefficient and the corresponding The bases of are multiplied, and then all the products are added to get the updated empirical waveform.
  • MSE as a cost function for the next iteration, until the maximum number of iterations or iteration termination conditions are reached, the final weight coefficient is multiplied by the corresponding basis, and the pump noise is removed from the received signal.
  • the calculation method of MSE is as follows:
  • w is the weight coefficient vector of each base
  • N is the number of symbols for this denoising
  • d i is the decision value of the i-th symbol
  • the physical meaning of MSE is the error power (Error Power) of the decoding output.
  • the particle swarm algorithm judges the direction of the particle's progress through the change trend of MSE, so as to find the best weight coefficient and improve the noise reduction effect.
  • the denoising output obtained after particle convergence is shown in Figure 6, and Figure 7 is an enlarged schematic diagram.
  • the frequency of the denoised signal is relatively clear, with a high degree of recognition and good denoising effect.
  • the method of the present invention can effectively eliminate the pump noise in the case of a single pump or a dual pump at the same frequency.
  • the method of the present invention is performed in the time domain to eliminate the periodic pump noise.

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Abstract

本发明公开了一种应用经验模态分解和粒子群优化算法的泵噪消除方法，该方法是基于泵噪声为一组基的线性组合的假设，在泵噪样本提取出来之后，利用经验模态分解将所提取的泵噪样本分解成一组信号作为基，通过粒子群优化算法可以找到这组基最佳线性组合的系数，更新泵噪样本，提升消噪效果。本发明在有限个消噪周期中，以加权的形式对当前泵噪样本进行修正，使其在有限的迭代次数内逐渐收敛于变化后的单位周期内的泵噪波形，以适应系统在长时间运行过程中泵噪声的缓慢变化。

Description

应用经验模态分解和粒子群优化算法的泵噪消除方法 技术领域
本发明属于无线随钻测井的技术领域，涉及一种应用经验模态分解(Empirical Mode Decomposition,EMD)和粒子群优化算法(Particle Swarm Optimization,PSO)的泵噪消除方法。
背景技术
目前，在无线随钻测量系统中，泥浆脉冲信号传输已经在世界范围得到广泛使用。泥浆脉冲，它是将井下仪器测量到的数据转换为电信号后，在泥浆泵作用下转换为的压力波信号，最后以泥浆为介质将信号传输至地面。其可靠性较高，传输距离远，更符合钻井的实际情况，是国内通用的传输方式。由于泥浆传输信号过程中需借助泥浆泵活塞不断往复运动，而在运动过程中，会产生周期性的泵噪声，因此对泥浆脉冲信号，必须消除泵噪声，才可进行信号的正确解码。泥浆脉冲通信系统属于时变系统。随着钻进深度的增加，包括泵噪声特性在内的泥浆信道参数可能持续性地发生变化。而泵噪声的周期性假设是建立在有限长度时间窗口内的近似性假设。随着系统运行时间的增加，所取得的泵噪样本与单位周期内泵噪声的波形之间的差异将逐渐增大，导致消噪输出中的残余噪声增加，影响消噪效果。
发明内容
本发明的目的在于针对现有技术的不足，提供一种应用经验模态分解和粒子群优化算法的泵噪消除方法，本发明利用经验模态分解和粒子群优化算法不断更新泵噪样本来达到更好的泵噪消除效果。
本发明的目的是通过以下技术方案来实现的：一种应用经验模态分解和粒子群优化算法的泵噪消除方法，其特征在于，该方法包括以下步骤：
(1)获取传感器获取测得的压力信号并进行低通滤波，得到滤除部分白噪声的泥浆压力信号。
(2)以泵冲传感器测得的泵冲信号作为时间基准，得到泵噪声信号的周期T；
(3)对步骤1的泥浆压力信号以步骤2的周期T为时间间隔进行分段截取，将所有分段信号求和并求平均；得到平均值最接近于周期性泵噪声在单个周期内实际波形的经验波形p(m)，即泵噪样本；
(4)对泵噪样本进行模态分解，得到构成泵噪的一组基；
(5)通过粒子群优化算法找到这组基最佳线性组合的系数，更新泵噪样本。
进一步地，所述步骤5具体为：对于粒子群优化算法，初始化权重系数为1，初始化PSO 参数，所述PSO参数包括权重系数的上下界、粒子个数及最大迭代次数等。然后开始解码过程的迭代。将接收信号减去泵噪声的经验波形后解码进行均衡判决，计算出其均方误差值(Mean Square Value,MSE)为输出反馈参量，每次利用优化算法迭代后，将更新的权重系数与相应的基对应相乘，然后将所有乘积相加得到更新之后的经验波形。按照同样的步骤计算MSE作为代价函数进行下一次迭代运算，直到达到最大迭代次数或迭代终止条件后，将最终的权重系数与相应的基对应相乘，得到用于从接收信号中消除泵噪声的最佳经验波形，输出最终解码符号。MSE的计算方式如下：
Figure PCTCN2019103602-appb-000001
其中，w为各个基的权重系数向量，N是本次消噪的符号个数，d i是第i个符号的判决值，
Figure PCTCN2019103602-appb-000002
是第i个符号的估计值。MSE的物理含义是解码输出的误差能量(Error Power)，粒子群算法通过MSE的变化趋势来判断粒子的前进方向，从而找到最佳的权重系数，提升消噪效果。
本发明的有益效果是，本发明应用EMD和PSO的泵噪消除方法基本思想是将泵噪看作一组基的线性组合，泵噪的更新过程是根据判决输出来确定出这组基的最佳线性组合方式。其中，EMD将泵噪样本分解成一组基，利用这组基可以重构出更贴近实际泵噪声的波形估计。同时，对于构成泵噪的任意一组基，PSO可以找到这组基最佳线性组合的系数，作为泵噪样本的更新机制。本发明在有限个消噪周期中，以加权的形式对当前泵噪样本进行修正，使其在有限的迭代次数内逐渐收敛于变化后的单位周期内的泵噪波形，以适应系统在长时间运行过程中泵噪声的缓慢变化。
附图说明
图1是基于EMD-PSO的泵噪消除方法结构图；
图2是传感器压力信号示意图；
图3是泵冲信号示意图；
图4是利用相干平均法得到的泵噪样本示意图；
图5是采用EMD分解泵躁样本得到的各路信号波形图；
图6是消噪输出信号示意图；
图7是消噪输出信号放大示意图。
具体实施方式
下面结合附图和具体实例对本发明做进一步的描述，但本发明的实施和保护范围不限于此。
图1是基于EMD-PSO的泵噪消除方法结构图，如图所示，对于井下传感器测得的压力信 号我们会依次进行低通滤波器、利用相干平均法提取泵噪声的经验波形，然后再使用EMD-PSO联合的迭代方法更新泵噪样本，直至与实际的泵噪声波形相符合。本例选取了一段实井双泵数据作为压力信号，波形如图2所示。双泵的基本频率分别是0.994Hz和1Hz，调制方式为FSK，码率13bps，深度2890m。
在从传感器获取测得的压力信号后，首先，根据压力数据特性确定低通滤波器性能指标并进行低通滤波，得到滤除部分白噪声的泥浆压力信号；
再通过引入图3所示的泵冲信号作为时间基准，得到泵噪声信号的周期T。泵冲信号由泵冲传感器测得。泵冲传感器是安装在泥浆泵上的位移传感器或者行程开关，用于记录泥浆泵活塞的位置信息。以行程开关类泵冲传感器为例，其输出一般为一组由矩形脉冲信号构成的开关量序列。低电平表示行程开关未被触发，高电平表示行程开关已被触发，每一个矩形脉冲的上升沿对应活塞到达行程开关处的时刻。对压力信号以T为时间间隔进行分段截取，将所有分段信号求和并求平均。在求和次数足够多的情况下，得到的平均值最接近于周期性泵噪声在单个周期内实际波形的经验波形，即泵噪样本，如图4所示；
接着对泵噪样本进行模态分解，得到如图5所示的构成泵噪的一组基及这组基对应的系数；
对于粒子群优化算法，初始化权重系数为1，初始化PSO参数，如权重系数的上下界，粒子个数及最大迭代次数等，然后开始解码过程的迭代。将接收信号减去泵噪声的经验波形后解码进行均衡判决，计算出其均方误差值(Mean Square Value,MSE)为输出反馈参量，每次利用优化算法迭代后，将更新的权重系数与相应的基对应相乘，然后将所有乘积相加得到更新之后的经验波形。按照同样的步骤计算MSE作为代价函数进行下一次迭代运算，直到达到最大迭代次数或迭代终止条件后，将最终的权重系数与相应的基对应相乘，得到用于从接收信号中消除泵噪声的最佳经验波形，输出最终解码符号。MSE的计算方式如下：
Figure PCTCN2019103602-appb-000003
其中w为各个基的权重系数向量，N是本次消噪的符号个数，d i是第i个符号的判决值，
Figure PCTCN2019103602-appb-000004
是第i个符号的估计值。MSE的物理含义是解码输出的误差能量(Error Power)，粒子群算法通过MSE的变化趋势来判断粒子的前进方向，从而找到最佳的权重系数，提升消噪效果。
本例中，粒子收敛后得到的消噪输出如图6所示，图7为其放大示意图。图中所示，消噪后的信号频率较为分明，可辨识度较高，消噪效果良好。
综上所述，本发明所提方法能够有效消除单泵或者双泵同频情况下的泵噪声，与现有技术相比，本发明方法在时域中进行，对于周期性的泵噪声的消除提供了一种可行性的解决方案，可适应系统在长时间运行过程中泵噪声出现的变化，提高解码准确度。

Claims (3)

  1. 一种应用经验模态分解和粒子群优化算法的泵噪消除方法，其特征在于，该方法包括以下步骤：
    (1)获取传感器获取测得的压力信号并进行低通滤波，得到滤除部分白噪声的泥浆压力信号。
    (2)以泵冲传感器测得的泵冲信号作为时间基准，得到泵噪声信号的周期T；
    (3)对步骤1的泥浆压力信号以步骤2的周期T为时间间隔进行分段截取，将所有分段信号求和并求平均；得到平均值最接近于周期性泵噪声在单个周期内实际波形的经验波形p(m)，即泵噪样本；
    (4)对泵噪样本进行模态分解，得到构成泵噪的一组基；
    (5)通过粒子群优化算法找到这组基最佳线性组合的系数，更新泵噪样本。
  2. 根据权利要求1所述的一种应用经验模态分解和粒子群优化算法的泵噪消除方法，其特征在于，所述步骤5具体为：对于粒子群优化算法，初始化权重系数为1，初始化PSO参数，然后开始解码过程的迭代。将接收信号减去泵噪声的经验波形后解码进行均衡判决，计算出其均方误差值MSE为输出反馈参量，每次利用优化算法迭代后，将更新的权重系数与相应的基对应相乘，然后将所有乘积相加得到更新之后的经验波形。按照同样的步骤计算MSE作为代价函数进行下一次迭代运算，直到达到最大迭代次数或迭代终止条件后，将最终的权重系数与相应的基对应相乘，得到用于从接收信号中消除泵噪声的最佳经验波形，输出最终解码符号。MSE的计算方式如下：
    Figure PCTCN2019103602-appb-100001
    其中，w为各个基的权重系数向量，N是本次消噪的符号个数，d i是第i个符号的判决值，
    Figure PCTCN2019103602-appb-100002
    是第i个符号的估计值。MSE的物理含义是解码输出的误差能量，粒子群算法通过MSE的变化趋势来判断粒子的前进方向，从而找到最佳的权重系数，提升消噪效果。
  3. 根据权利要求2所述的一种应用经验模态分解和粒子群优化算法的泵噪消除方法，其特征在于，所述PSO参数包括权重系数的上下界、粒子个数及最大迭代次数等。
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