Globally optimized seismic impedance inversion with lateral constraints and its application

ZHU Jian-Bing; GAO Zhao-Qi; TIAN Ya-Jun; LIANG Xing-Cheng

doi:10.11720/wtyht.2022.1585

2022 Vol. 46, No. 6

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Article navigation > Geophysical and Geochemical Exploration > 2022 > 46(6): 1477-1484

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ZHU Jian-Bing, GAO Zhao-Qi, TIAN Ya-Jun, LIANG Xing-Cheng. 2022. Globally optimized seismic impedance inversion with lateral constraints and its application. Geophysical and Geochemical Exploration, 46(6): 1477-1484. doi: 10.11720/wtyht.2022.1585

Citation:

ZHU Jian-Bing, GAO Zhao-Qi, TIAN Ya-Jun, LIANG Xing-Cheng. 2022. Globally optimized seismic impedance inversion with lateral constraints and its application. Geophysical and Geochemical Exploration, 46(6): 1477-1484. doi: 10.11720/wtyht.2022.1585

Globally optimized seismic impedance inversion with lateral constraints and its application

1. Geophysical Research Institute,Shengli Oilfield Branch Company of Sinopec,Dongying 257022,China；
2. School of Information and Communications Engineering,Xi'an Jiaotong University,Xi'an 710049,China

Received Date: 01 November 2021

Revised Date: 20 December 2022

Publish Date: 03 January 2023

Abstract

Abstract

The seismic impedance inversion is nonlinear optimization based on seismic data to obtain wave impedance parameters.The global optimization algorithm independent of the gradient information of objective function is an effective method for seismic impedance inversion.However,this method adopts a trace-by-trace inversion strategy and ignores the spatial correlation of adjacent seismic traces,resulting in poor lateral continuity of the inversion results.Given this,this study proposed a model space initialization method integrating the optimal solution of the bypass to restrict the search space of wave impedance inversion,in order to improve the lateral continuity of inversion results.Based on this,this study proposed a seismic impedance inversion method with lateral constraints based on multi-group variation differential evolution.A case of synthetic seismogram shows that this method has a higher convergence rate and better lateral continuity of inversion results than conventional methods.This method was applied to the inversion of reservoir impedance parameters of a block of the Shengli Oilfield.The obtained inversion results were in good agreement with the logging data and effectively characterize the thickness of reservoir sandstone.
- seismic impedance inversion /
- global optimization /
- lateral continuity /
- oil reservoir geophysics /
- seismic data processing

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References

[1]	万晓明, 梁劲, 梁金强, 等. 叠后波阻抗无井反演技术在T研究区天然气水合物分布预测中的应用[J]. 物探与化探, 2016, 40(3):438-444. Google Scholar
[2]	Wan X M, Liang J, Liang J Q, et al. The application of post-stack impedance inversion without well to the prediction of gas hydrate distribution in T study area[J]. Geophysical and Geochemical Exploration, 2016, 40(3):438-444. Google Scholar
[3]	孔省吾, 张云银, 沈正春, 等. 波形指示反演在灰质发育区薄互层浊积岩预测中的应用——以牛庄洼陷沙三中亚段为例[J]. 物探与化探, 2020, 44(3):665-671. Google Scholar
[4]	Kong X W, Zhang Y Y, Shen Z C, et al. The application of waveform inversion prediction of thin turbidite reservoir to calcareous depositional area: A case study of E3S32 in Niuzhuang sag[J]. Geophysical and Geochemical Exploration, 2020, 44(3): 665-671. Google Scholar
[5]	汪玲玲, 高静怀, 赵谦, 等. 基于矩阵Toeplitz稀疏分解的相对波阻抗反演方法[J]. 地球物理学报, 2017, 60(2):639-654. Google Scholar
[6]	Wang L L, Gao J H, Zhao Q, et al. Relative acoustic impedance inversion via Toeplitz-Sparse Matrix Factorization[J]. Chinese Journal of Geophysics, 2017, 60(2):639-654. Google Scholar
[7]	Frank M S, Balanis C A. A conjugate direction method for geophysical inversion problems[J]. IEEE Transactions on Geoscience and Remote Sensing, 1987,(6):691-701. Google Scholar
[8]	Deift P, Zhou X. A steepest descent method for oscillatory Riemann-Hilbert problems[J]. Appeared in Bulletin of the American Mathematical Society, 1992, 26(1):119-124. Google Scholar
[9]	Ma X Q. Simultaneous inversion of prestack seismic data for rock properties using simulated annealing[J]. Geophysics, 2002, 67(6):1877-1885. Google Scholar
[10]	Lagos S R, Sabbione J I, Velis D R. Very fast simulated annealing and particle swarm optimization for microseismic event location[C]// SEG Technical Program Expanded Abstracts 2014, 2014. Google Scholar
[11]	Donelli M, Franceschini G, Martini A, et al. An integrated multiscaling strategy based on a particle swarm algorithm for inverse scattering problems[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(2):298-312. Google Scholar
[12]	Fernandez Martinez J L, Mukerji T, Garcia Gonzalo E, et al. Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers[J]. Geophysics, 2012, 77(1):M1-M16. Google Scholar
[13]	Parolai S, Picozzi M, Richwalski S M, et al. Joint inversion of phase velocity dispersion and H/V ratio curves from seismic noise recordings using a genetic algorithm,considering higher modes[J]. Geophysical Research Letters, 2005, 32(1). Google Scholar
[14]	Govindan R, Kumar R, Basu S, et al. Altimeter-derived ocean wave period using genetic algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 8(2):354-358. Google Scholar
[15]	Li T, Mallick S. Multicomponent,multi-azimuth pre-stack seismic waveform inversion for azimuthally anisotropic media using a parallel and computationally efficient non-dominated sorting genetic algorithm[J]. Geophysical Journal International, 2015, 200(2):1136-1154. Google Scholar
[16]	Semnani A, Kamyab M, Rekanos I T. Reconstruction of one-dimensional dielectric scatterers using differential evolution and particle swarm optimization[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4):671-675. Google Scholar
[17]	Dehmollaian M. Through-wall shape reconstruction and wall parameters estimation using differential evolution[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 8(2):201-205. Google Scholar
[18]	Pan Z, Wu J, Gao Z, et al. Adaptive differential evolution by adjusting subcomponent crossover rate for high-dimensional waveform inversion[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(6):1327-1331. Google Scholar
[19]	Li Z, Hao T, Xu Y, et al. A global optimizing approach for waveform inversion of receiver functions[J]. Computers & Geosciences, 2010, 36(7): 871-880. Google Scholar
[20]	Potter M A, Jong K. A cooperative coevolutionary approach to function optimization[C]// Springer: Parallel Problem Solving from Nature,Heidelberg, 1994. Google Scholar
[21]	Wang C, Gao J. High-dimensional waveform inversion with cooperative coevolutionary differential evolution algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 9(2):297-301. Google Scholar
[22]	Gao Z, Pan Z, Gao J. Multimutation differential evolution algorithm and its application to seismic inversion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6):3626-3636. Google Scholar
[23]	Gholami A. Nonlinear multichannel impedance inversion by total-variation regularization[J]. Geophysics, 2015, 80(5):R217-R224. Google Scholar
[24]	Zhang S, Fan X, Li G, et al. Multi-trace blocky reflectivity inversion with anisotropic total variation regularization[C]// SEG Technical Program Expanded Abstracts 2019, 2019. Google Scholar
[25]	Cheng L, Wang S, Li S, et al. Multi-trace nonstationary sparse inversion with structural constraints[J]. Acta Geophysica, 2020, 68(3):675-685. Google Scholar