Citation: | MA Shanshan, LI Weiqin, YANG Zhaoxiang, et al. Seismoelectric logging signal detection based on stochastic resonance system[J]. Petroleum Drilling Techniques, 2025, 53(0):1−8. DOI: 10.11911/syztjs.2025045 |
In order to solve the problem that the traditional stochastic resonance system can only process low-frequency signals and cannot directly process high-frequency seismoelectric logging signals, the dynamic equation of the nonlinear bistable stochastic resonance system is improved by using the phase trajectory time-scale transformation. By analyzing the numerical simulation model and frequency domain diagram of the numerical simulation model and the output signal of the circuit, it is found that the output signal of the system has two obvious stable states, The peak of the spectrum at the frequency of the signal under test is significantly increased, the amplitude and energy of the output signal are significantly enhanced, and the signal-to-noise ratio of the measured signal is improved. A stochastic resonance system with the output signal-to-noise ratio as the evaluation function and the genetic algorithm is used to optimize the system parameters to obtain the optimal output, and the output signal signal-to-noise ratio is improved by 23.5642 dB. Finally, the optimization system is applied to the seismic logging signal, and the amplitude at the eigenfrequency of the output signal of the random resonance system is 44 times that of the traditional linear filtering technology. The results show that The bistable stochastic resonance system based on the phase trajectory time-scale transformation can directly process the high-frequency seismic logging signal, weaken the noise in the signal, and significantly improve the clarity and quality of the signal.
[1] |
王诚至. 基于震电测井的信号收发电路系统研究[D]. 成都:电子科技大学,2022.
WANG Chengzhi. Research on signal transceiver circuit system based on seismo-electric logging[D]. Chengdu: University of Electronic Science and Technology of China, 2022.
|
[2] |
BIOT M A. Theory of propagation of elastic waves in a fluid-saturated porous solid. II. Higher frequency range[J]. The Journal of the Acoustical Society of American, 1956, 28(2): 179–191. doi: 10.1121/1.1908241
|
[3] |
GARAMBOIS S, DIETRICH M. Full waveform numerical simulations of seismoelectromagnetic wave conversions in fluid-saturated stratified porous media[J]. Journal of Geophysical Research: Solid Earth, 2002, 107(B7): ESE 5-1-ESE 5-18.
|
[4] |
胡文亮,张国栋,刘保银,等. 基于微电阻率成像测井的地层各向异性表征方法[J]. 石油钻探技术,2023,51(2):125–130. doi: 10.11911/syztjs.2023048
HU Wenliang, ZHANG Guodong, LIU Baoyin, et al. Formation anisotropy characterization method based on micro-resistivity imaging logging[J]. Petroleum Drilling Techniques, 2023, 51(2): 125–130. doi: 10.11911/syztjs.2023048
|
[5] |
孙小芳,刘峰,张聪慧,等. 慢速地层偶极声波远探测井眼成像发射频率优选[J]. 石油钻探技术,2023,51(1):98–105. doi: 10.11911/syztjs.2023017
SUN Xiaofang, LIU Feng, ZHANG Conghui, et al. Emission frequency optimization of borehole imaging for dipole acoustic remote detection of slow formations[J]. Petroleum Drilling Techniques, 2023, 51(1): 98–105. doi: 10.11911/syztjs.2023017
|
[6] |
康正明,秦浩杰,张意,等. 基于LSTM神经网络的随钻方位电磁波测井数据反演[J]. 石油钻探技术,2023,51(2):116–124. doi: 10.11911/syztjs.2023047
KANG Zhengming, QIN Haojie, ZHANG Yi, et al. Data inversion of azimuthal electromagnetic wave logging while drilling based on LSTM neural network[J]. Petroleum Drilling Techniques, 2023, 51(2): 116–124. doi: 10.11911/syztjs.2023047
|
[7] |
祁晓,张璋,李东,等. 基于阵列声波测井技术的海上砂岩储层压裂效果评价方法[J]. 石油钻探技术,2023,51(6):128–134.
QI Xiao, ZHANG Zhang, LI Dong, et al. Evaluation of fracturing effects in offshore sandstone reservoirs based on array acoustic logging technology[J]. Petroleum Drilling Techniques, 2023, 51(6): 128–134.
|
[8] |
马通,祝鹏,陈鸣,等. 琼东南盆地天然气水合物储层参数测井评价及分析[J]. 断块油气田,2023,30(2):254–260.
MA Tong, ZHU Peng, CHEN Ming, et al. Logging evaluation and analysis of reservoir parameter for natural gas hydrate in Qiongdongnan Basin[J]. Fault-Block Oil and Gas Field, 2023, 30(2): 254–260.
|
[9] |
付德奎. 普光气田超深高含硫水平井开发测井先导性试验[J]. 断块油气田,2023,30(6):1007–1012.
FU Dekui. Pilot test for development logging of ultra-deep high sulfur horizontal wells in Puguang Gas Field[J]. Fault-Block Oil and Gas Field, 2023, 30(6): 1007–1012.
|
[10] |
赵辉,齐怀彦,王凯,等. 致密砂岩油藏测井响应特征及有利区评价[J]. 特种油气藏,2023,30(5):35–41. doi: 10.3969/j.issn.1006-6535.2023.05.005
ZHAO Hui, QI Huaiyan, WANG Kai, et al. Characteristics of well logging response and evaluation of favorable zones in tight sandstone reservoirs[J]. Special Oil & Gas Reservoirs, 2023, 30(5): 35–41. doi: 10.3969/j.issn.1006-6535.2023.05.005
|
[11] |
段韵达,胡恒山,关威. 矿化度界面对震电测井波场的影响[J]. 地球物理学报,2020,63(2):778–787. doi: 10.6038/cjg2019M0623
DUAN Yunda, HU Hengshan, GUAN Wei. Influence of salinity interface on seismoelectric logging wave field[J]. Chinese Journal of Geophysics, 2020, 63(2): 778–787. doi: 10.6038/cjg2019M0623
|
[12] |
GUAN Wei, HU Hengshan. Finite-difference modeling of the electroseismic logging in a fluid-saturated porous formation[J]. Journal of Computational Physics, 2008, 227(11): 5633–5648. doi: 10.1016/j.jcp.2008.02.001
|
[13] |
MADHUKAR P S, MADHUKAR S. Kalman filters in different biomedical signals-an overview[C]//2020 International Conference on Smart Electronics and Communication (ICOSEC). Piscataway, NJ: IEEE Press, 2020: 1268-1272.
|
[14] |
陆秋平. 基于相关原理的信号检测方法及其应用研究[D]. 杭州:浙江大学,2011.
LU Qiuping. Study on signal detection method and its application based on correlative theory[D]. Hangzhou: Zhejiang University, 2011.
|
[15] |
陈韶华,相敬林. 一种改进的时域平均法检测微弱信号研究[J]. 探测与控制学报,2003,25(4):56–59. doi: 10.3969/j.issn.1008-1194.2003.04.013
CHEN Shaohua, XIANG Jinglin. A modified time averaging method in weak signal detection[J]. Journal of Detection & Control, 2003, 25(4): 56–59. doi: 10.3969/j.issn.1008-1194.2003.04.013
|
[16] |
NICHOGA V, PAVLYSH V, ROMANYSHYN Y. Features of use wavelet transforms for processing and analysis of rail fault detection signals[C]//2010 International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET). Piscataway, NJ: IEEE Press, 2010: 295-295.
|
[17] |
XIONG Zhangliang, SONG Yaoliang, LIU Ming, et al. Signal detection with chaotic neural network[C]//International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003. Piscataway, NJ: IEEE Press, 2003: 164-167.
|
[18] |
XING Yingchun, ZHANG Futao. Simulation and application of weak signal detection based on Chaos theory[C]//2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Piscataway, NJ: IEEE Press, 2017: 2434-2437.
|
[19] |
冷永刚,王太勇. 二次采样用于随机共振从强噪声中提取弱信号的数值研究[J]. 物理学报,2003,52(10):2432–2437. doi: 10.3321/j.issn:1000-3290.2003.10.014
LENG Yonggang, WANG Taiyong. Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy noise[J]. Acta Physica Sinica, 2003, 52(10): 2432–2437. doi: 10.3321/j.issn:1000-3290.2003.10.014
|
[20] |
彭敏玲. 超高频微弱信号检测中调参随机共振的应用研究[J]. 化工管理,2017(32):56–58. doi: 10.3969/j.issn.1008-4800.2017.32.054
PENG Minling. Research on the application of modulated parameter stochastic resonance in ultra-high frequency weak signal detection[J]. Chemical Enterprise Management, 2017(32): 56–58. doi: 10.3969/j.issn.1008-4800.2017.32.054
|
[21] |
WU Aiping, MWACHAKA S M, PEI Yanliang, et al. A novel weak signal detection method of electromagnetic LWD based on a duffing oscillator[J]. Journal of Sensors, 2018, 2018: 5847081.
|
[22] |
GAO Fangzheng, HUANG Jiacai, WU Yuqiang, et al. A time-scale transformation approach to prescribed-time stabilisation of non-holonomic systems with inputs quantisation[J]. International Journal of Systems Science, 2022, 53(8): 1796–1808. doi: 10.1080/00207721.2021.2024296
|
[23] |
ZHENG Yongjun, HUANG Ming, LU Yi, et al. Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm[J]. Neural Computing and Applications, 2020, 32(22): 16807–16818.
|
[24] |
AVINASH KHATRI K C, SHAH K B, LOGESHWARAN J, et al. Genetic algorithm based techno-economic optimization of an isolated hybrid energy system[J]. ICTACT Journal on Microelectronics, 2023, 8(4): 1447–1450.
|
[25] |
杨波,夏虹,尹文哲,等. 自适应随机共振在信号特征提取中的应用[J]. 哈尔滨工程大学学报,2022,43(12):1750–1758.
YANG Bo, XIA Hong, YIN Wenzhe, et al. Application of adaptive stochastic resonance in signal feature extraction[J]. Journal of Harbin Engineering University, 2022, 43(12): 1750–1758.
|