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Coal Geology & Exploration

Abstract

The seismic resolution has long been limited to the quarter wavelength. To compress the seismic wavelet, the key problems are signal-to-noise ratio (SNR) and resolution in early stages, and high resolution and high fidelity in late stages. Meanwhile, by using well data, many inversion methods of spanning quarter wavelength have been developed. The success of these methods depends on the preservation of seismic wave amplitude, the accuracy of seismic interpretation, and the compatibility between well data and seismic data. Afterward, researchers achieved seismic inversion without well data constraint, and the corresponding resolution reached the level of reflectivity. Meanwhile, the key problem turned into the credibility of impedance information. In this study, we sort out relevant seismic data and refer to the thoughts and methods of seismic sedimentology, and propose a thin fluvial channel sand body depiction method without well data constraint. This method not only exceeds the limit of quarter wavelength but also guarantees the accuracy of seismic interpretation. Case study is carried out and quarter-wavelength-based superimposed channels are separated. This method shows great prospects for old data mining and new data processing for petroleum and coal companies. In particular, this method provides a new solution to production problems such as the determination of super-thin reservoirs and super-close faults, and the detection of goaf and abandoned tunnels.

Keywords

resolution, quarter wavelength, thin reservoir, fluvial channel separation

DOI

10.12363/issn.1001-1986.22.11.0841

Reference

[1] 王承曙,张裕平. 自适应的最小平方反褶积及在地震勘探中的应用[J]. 地球物理学报,1987,30(3):307−317.

WANG Chengshu,ZHANG Yuping. Adaptive least square deconvolution and application to seismic prospecting[J]. Chinese Journal of Geophysics,1987,30(3):307−317.

[2] LEINBACH J. Wiener spiking deconvolution and minimum:A tutorial[J]. The Leading Edge,1995,14(3):189−192.

[3] PEACOCK K L,TREITEL S. Predictive deconvolution:Theory and practice[J]. Geophysics,1969,34(2):155−169.

[4] 王君,周兴元,曹孟起. 同态反褶积的改进与应用[J]. 石油地球物理勘探,2003,38(增刊1):27−30.

WANG Jun,ZHOU Xingyuan,CAO Mengqi. Improvement and application of homomorphic deconvolution[J]. Oil Geophysical Prospecting,2003,38(Sup.1):27−30.

[5] LI Hao,LI Guofa,MA Xiong,et al. Multichannel deconvolution with spatial reflection regularization[J]. Applied Geophysics,2021,18(1):85−93.

[6] TAYLOR H L,BANKS S C,MCCOY J F. Deconvolution with the L1 norm[J]. Geophysics,1979,44(1):39−52.

[7] DEBEYE H W J,RIEL P V. Lp–norm deconvolution[J]. Geophysical Prospecting,1990,38(4):381−403.

[8] 张繁昌,刘杰,印兴耀,等. 修正柯西约束地震盲反褶积方法[J]. 石油地球物理勘探,2008,43(4):391−396.

ZHANG Fanchang,LIU Jie,YIN Xingyao,et al. Modified cauchy−constrained seismic blind deconvolution[J]. Oil Geophysical Prospecting,2008,43(4):391−396.

[9] MA Ming,WANG Shangxu,YUAN Sanyi,et al. Multichannel spatially correlated reflectivity inversion using block sparse Bayesian learning[J]. Geophysics,2017,82(4):V191−V199.

[10] 孟大江,王德利,冯飞,等. 基于Curvelet变换的稀疏反褶积[J]. 石油学报,2013,34(1):107−114.

MENG Dajiang,WANG Deli,FENG Fei,et al. Sparse deconvolution based on the curvelet transform[J]. Acta Petrolei Sinica,2013,34(1):107−114.

[11] 刘成明. 基于Shearlet稀疏约束地震数据处理方法研究[D]. 长春:吉林大学,2018.

LIU Chengming. Research on seismic data processing methods based on shearlet sparse constraint[D]. Changchun:Jilin University,2018.

[12] CHEN Siyuan,CAO Siyuan,SUN Yaoguang. Enhancing the resolution of seismic data based on the non–local similarity[J]. Geophysical Prospecting,2022,70:1116−1128.

[13] MARGRAVE G F,LAMOUREUX M P,HENLEY D C. Gabor deconvolution:Estimating reflectivity by nonstationary deconvolution of seismic data[J]. Geophysics,2011,76(3):W15−W30.

[14] 尹燕法. 小波分析提高地震勘探资料分辨率的研究[D]. 青岛:山东科技大学,2009.

YIN Yanfa. Study of data processing of high–density resistivity method in coal mine and it’s application[D]. Qingdao:Shandong University of Science and Technology,2009.

[15] STOCKWELL R G,MANSINHA L,LOWE R P. Localization of the complex spectrum:The S transform[J]. IEEE Transactions on Signal Processing,1996,44(4):998−1001.

[16] HUANG N E,SHEN Zheng,LONG S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non–stationary time series analysis[J]. Proceeding of the Royal Society of London,1998,454:903−995.

[17] DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531−544.

[18] HARGREAVES N D,CALVERT A J. Inverse Q filtering by fourier transform[J]. Geophysics,1991,56(4):519−527.

[19] ZHANG Changjun,ULRYCH T J. Seismic absorption compensation:A least squares inverse scheme[J]. Geophysics,2007,72(6):R109−R114.

[20] WANG Shoudong,CHEN Xiaohong. Absorption–compensation method by I1–norm regularization[J]. Geophysics,2014,79(3):V107−V114.

[21] JIANG Y,MA Y,CAO Siyuan,et al. An improved method for Q factor estimation based on frequency weighted exponential function[C]//80th EAGE Conference and Exhibition. Extended Abstracts,2018.

[22] XUE Yajuan,CAO Junxing,WANG Xingjian. Inverse Q filtering via synchrosqueezed wavelet transform[J]. Geophysics,2019,84(2):V121−V132.

[23] LIU Guochang,LI Chao,RAO Ying,et al. Oriented pre–stack inverse Q filtering for resolution enhancements of seismic data[J]. Geophysical Journal International,2020,223:488−501.

[24] RONNEBERGER O,FISCHER P,BROX T. U–Net:Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer. Springer International Publishing,2015.

[25] 孙永壮,黄鋆,俞伟哲,等. 基于U–Net网络的端到端地震高分辨率处理技术[J]. 地球物理学进展,2021,36(3):1297−1305.

SUN Yongzhuang,HUANG Jun,YU Weizhe,et al. End–to–end high–resolution seismic processing method based on U–Net network[J]. Progress in Geophysics,2021,36(3):1297−1305.

[26] CHEN D,GAO J,GAO Z,et al. Reflectivity–GAN:A data–driven method for seismic deconvolution[C]//EAGE 2020 Annual Conference & Exhibition Online. European Association of Geoscientists & Engineers,2020.

[27] PEREG D,COHEN I,VASSILIOU A A. Sparse seismic deconvolution via recurrent neural network[J]. Journal of Applied Geophysics,2020,175:103979.

[28] CHAI Xintao,TANG Genyang,LIN Kai,et al. Deep learning for multitrace sparse−spike deconvolution[J]. Geophysics,2021,86(3):V207−V218.

[29] PHAN S,SEN M K. Seismic nonstationary deconvolution with physics–guided autoencoder[C]//SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy. OnePetro,2021.

[30] GAO Zhaoqi,HU Sichao,LI Chuang,et al. A deep–learning–based generalized convolutional model for seismic data and its application in seismic deconvolution[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60:1−17.

[31] WIDESS M B. How thin is a thin bed?[J]. Geophysics,1973,38(6):1176−1180.

[32] 李庆忠. 走向精确勘探的道路:高分辨率地震勘探系统工程剖析[M]. 北京:石油工业出版社,1994.

[33] 汪恩华,贺振华,李庆忠. 薄储层厚度计算新方法探索[J]. 物探化探计算技术,2001,23(1):22−25.

WANG Enhua,HE Zhenhua,LI Qingzhong. Approach to the thickness determination of thin beds[J]. Computing Techniques for Geophysical and Geochemical Exploration,2001,23(1):22−25.

[34] CASTAGNA J P,SUN Shengjie,SIEGFRIED R W. Instantaneous spectral analysis:Detection of low–frequency shadows associated with hydrocarbons[J]. The Leading Edge,2012,22(2):120−127.

[35] 高静怀,陈文超,李幼铭,等. 广义S变换与薄互层地震响应分析[J]. 地球物理学报,2003,46(4):526−532.

GAO Jinghuai,CHEN Wenchao,LI Youming,et al. Generalized S transform and seismic response analysis of thin interbeds[J]. Chinese Journal of Geophysics,2003,46(4):526−532.

[36] PURYEAR C I,TAI Shenghong,CASTAGNA J P. Comparison of frequency attributes from CWT and MPD spectral decompositions of a complex turbidite channel model[C]//SEG Las Vegas 2008 Annual Meeting,2008:393–397.

[37] 张繁昌,何晋越,桑凯恒,等. 稀疏反射系数频率域正余弦分量协同反演方法[J]. 石油地球物理勘探,2018,53(4):778−783.

ZHANG Fanchang,HE Jinyue,SANG Kaiheng,et al. A sparse reflectivity sine–cosine synergistic inversion method in the frequency domain[J]. Oil Geophysical Prospecting,2018,53(4):778−783.

[38] ZENG Hongliu. Predicting geometry and stacking pattern of thin beds by interpreting geomorphology and waveforms using sequential stratal–slices in the Wheeler domain[J]. Interpretation,2015,3(3):SS49−SS64.

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