AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE026160068
Submit to JSE
Cite this article
27
Download
620
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Fluid mobility attribute based on frequency-corrected generalized S-transform and its application in subtle reservoir delineation

Zezhou Zhang1 ,  Naihao Liu1* ,  Shengjun Li2* ,  Jinghuai Gao1 ,  Nian Liu3
Show Less
1 School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
2 PetroChina Research Institute of Petroleum Exploration and Development, Northwest, China National Petroleum Corporation (CNPC), Lanzhou, Gansu, China
3 Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi’an, Shaanxi, China
JSE, 026160068 https://doi.org/10.36922/JSE026160068
Received: 16 April 2026 | Revised: 21 May 2026 | Accepted: 27 May 2026 | Published online: 3 July 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

With the continuous advancement of oil and gas exploration, the focus has gradually shifted from conventional structural reservoirs to lithologic reservoirs. However, lithologic reservoirs are often deeply buried and thinly layered, exhibiting weak seismic responses. In addition, strong lateral heterogeneity in their spatial distribution makes them increasingly subtle and difficult to characterize, posing significant challenges for reservoir detection. To address the problem of accurately detecting subtle reservoirs, this study proposes a subtle reservoir detection workflow based on the fluid mobility attribute derived from the frequency-corrected generalized S-transform (FCGST). The workflow utilizes the high-resolution and high-precision time–frequency spectrum generated by FCGST to extract fluid mobility attributes from seismic data, thereby effectively characterizing the spatial distribution of subtle reservoirs. The effectiveness of the proposed workflow is validated using both synthetic models and field seismic data from the Permian Maokou Formation in the Sichuan Basin. Compared with conventional generalized S-transform-based approaches, the proposed method reduces the dominant-frequency estimation error from approximately 30% to less than 3% under different signal-to-noise ratio (SNR) conditions in synthetic tests, while significantly improving the spatial focusing of reservoir-related anomalies. In synthetic tests under noisy conditions (SNR as low as 5 dB), the FCGST-based attribute exhibits enhanced robustness and maintains clearer delineation of reservoir boundaries. The proposed workflow demonstrates strong potential for practical exploration applications, providing effective support for subtle reservoir characterization, well placement, and horizontal well trajectory optimization.

Keywords
Fluid mobility attribute
Frequency-corrected generalized S-transform
Subtle reservoir
Maokou formation
Sichuan Basin
Funding
This research was supported in part by the National Natural Science Foundation of China under Grant 42274144, in part by the Prospective, Basic, and Strategic Technology Research Project of PetroChina under Grant 2021DJ0606, in part by the Gansu Science and Technology Program under Grant 23ZDGA004, and in part by the Open Fund of Shaanxi Key Laboratory of Petroleum Accumulation Geology under Grant PAG-202404.
Conflict of interest
Naihao Liu serves as a Guest Editor for this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. The authors declare they have no competing interests.
References
  1. Jia C, Zhao W, Zou C, et al. Geological theory and exploration technology for lithostratigraphic hydrocarbon reservoirs. Pet Explor Dev. 2007;34(3):1474-0. [In Chinese]

 

  1. Guo Q, Ba J, Luo C, Pang M. Seismic rock physics inversion with varying pore aspect ratio in tight sandstone reservoirs. J Pet Sci Eng. 2021;207:109131. doi: 10.1016/j.petrol.2021.109131

 

  1. Liu N, Gao J, Zhang B, Wang Q, Jiang X. Self-adaptive generalized S-transform and its application in seismic time-frequency analysis. IEEE Trans Geosci Remote Sens. 2019;57(10):7849-7859. doi: 10.1109/TGRS.2019.2916792

 

  1. Guo Q, Ba J, Luo C, Chen J. Seismic rock-physics simultaneous inversion for petrophysical and pore parameters of carbonate reservoirs with Gaussian mixture model. Chin J Geophys. 2024;67(10):3989-4004. [In Chinese] doi: 10.6038/cjg2022Q0756

 

  1. Li S, Gao J, Lou Y, Gui J, He D, Chang D. Separation and suppression of strong reflections via a multiscale attention deep learning model. IEEE Trans Geosci Remote Sens. 2024;62:1-12. doi: 10.1109/TGRS.2024.3376336

 

  1. Zhou J, Ba J, Castagna J, Guo Q, Yu C, Jiang R. Application of an STFT-based seismic even and odd decomposition method for thin-layer property estimation. Geosci Remote Sens Lett. 2019;16(9):1348-1352. doi: 10.1109/LGRS.2019.2901261

 

  1. Zhou Z, Wang G, Ran Y, Lai J, Cui Y, Zhao X. A logging identification method of tight oil reservoir lithology and lithofacies: A case from Chang7 Member of Triassic Yanchang Formation in Heshui area, Ordos Basin, NW China. Petroleum Explor Dev. 2016;43(1):65-73. doi: 10.1016/S1876-3804(16)30007-6

 

  1. Xiang M, Qin P, Zhang F. Research and application of logging lithology identification for igneous reservoirs based on deep learning. J. Appl. Geophys. 2020;173:103929. doi: 10.1016/j.jappgeo.2019.103929

 

  1. Li K, Zong J, Fei Y, Liang J, Hu G. Simultaneous seismic deep attribute extraction and attribute fusion. IEEE Trans Geosci Remote Sens. 2021;60:1-10. doi: 10.1109/TGRS.2021.3113075

 

  1. Cui Y, Chen Y, Xu C, et al. Identification and characterization of fractured cavities in carbonate reservoirs using Swin- UNet transformer and seismic attribute compression fusion. J Seism Explor. 2026;35(1),119–138. doi: 10.36922/JSE025420090

 

  1. Chen G, Shi Y, Yang H, et al. Fault identification method of attribute fusion based on seismic optimized frequency of seismic data. Chin J Geophys. 2023;66(3):1232-1243. [In Chinese] doi: 10.6038/cjg2022Q0799

 

  1. Liu N, Zhang Z, Zhang H, et al. Multiple-frequency attribute blending via adaptive uniform manifold approximation and projection and its application on hydrocarbon reservoir delineation. Geophysics. 2024;89(1):WA195-WA206. doi: 10.1190/geo2023-0111.1

 

  1. Chen X, He Z, Zhu S, Liu W, Zhong W. Seismic low-frequency-based calculation of reservoir fluid mobility and its applications. Appl Geophys. 2012;9(3):326-332. doi: 10.1007/s11770-012-0340-6

 

  1. Luo Y, Huang H, Yang Y, Hao Y, Zhang S, Li Q. Integrated prediction of deepwater gas reservoirs using Bayesian seismic inversion and fluid mobility attribute in the South China Sea. J Nat Gas Sci Eng. 2018;59:56-66. doi: 10.1016/j.jngse.2018.08.019

 

  1. Guo Z, Li Y, Liu C, Zhang D, Li A. Characterization of a volcanic gas reservoir using seismic dispersion and fluid mobility attributes. Lithosphere. 2021;2021(Special 3):9520064. doi: 10.2113/2021/9520064

 

  1. Zeng J, Stovas A, Huang H, Ren L, Tang T. Prediction of shale gas reservoirs using fluid mobility attribute driven by post-stack seismic data: A case study from Southern China. Appl Sci. 2020;11(1):219. doi: 10.3390/app11010219

 

  1. Wang Y, Pan X, Shao K, Wang N, Zhang Y, Zhang X. Fluid mobility attribute extraction based on optimized second-order synchroextracting wavelet transform. IEEE Geosci Remote Sens Lett. 2025;22:1-5. doi: 10.1109/LGRS.2025.3607097

 

  1. Silin D, Korneev V, Goloshubin G, Patzek T. A hydrologic view on Biot’s theory of poroelasticity. Lawrence Berkeley National Laboratory. Accessed January 15, 2026. https:// escholarship.org/uc/item/13x2t95p

 

  1. Korneev V, Silin D, Goloshubin G, Vingalov V. (2004). Seismic imaging of oil production rate.In: Proceedings of the SEG International Exposition and Annual Meeting, October 10, 2004, Denver, Co, USA. 2004:1476-1479. doi: 10.1190/1.1843299

 

  1. Goloshubin G, Van Schuyver C, Korneev V, Silin D, Vingalov V. Reservoir imaging using low frequencies of seismic reflections. Leading Edge. 2006;25(5):527-531. doi: 10.1190/1.2202652

 

  1. Dai S, Chen Z, Yu J, Xie J, Liu Y. Application of the fluid mobility attribute technique in the reservoir characterization of KG Oilfield, Algeria. Pet Explor Dev. 2010;37(5):1865-0. [In Chinese]

 

  1. Zhang J, Wang Z, Tan M, Yu Z, Zhao J, Tian Z. Mobility attribute extraction and its application in a deep reservoir at Dongying Sag. Geophys Prospect Pet. 2020;59(3):441-449. [In Chinese] doi: 10.3969/j.issn.1000-1441.2020.03.013

 

  1. Zhang Y, Wen X, Jiang L, Liu J, Yang J, Liu S. Prediction of high-quality reservoirs using the reservoir fluid mobility attribute computed from seismic data. J Petrol Sci Eng. 2020;190:107007. doi: 10.1016/j.petrol.2020.107007

 

  1. Gabor D. Theory of communication. Part 1: The analysis of information. J Inst Electr Eng Part III: Radio Commun Eng. 1946;93(26):429-441. doi: 10.1049/ji-3-2.1946.0074

 

  1. Lu W, Li F. Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram. Geophysics. 2013;78(2):V43-V51. doi: 10.1190/geo2012-0125.1

 

  1. Shang S, Fu S. Spectral decomposition of seismic data with improved synchrosqueezing time-frequency transform. J Seism Explor. 2022;31(1),53-64.

 

  1. Morlet J, Arens G, Fourgeau E, Giard D. Wave propagation and sampling theory-Part I: Complex signal and scattering in multilayered media. Geophysics. 1982;47(2):203-221. doi: 10.1190/1.1441328

 

  1. Wang P, Gao J. Wavelet frame based seismic attributes extraction using a filtering scheme. J Seism Explor. 2013;22(4),353-372.

 

  1. Lin P, Peng S, Zhao J, Cui X, Wang H. L1-norm regularization and wavelet transform: An improved plane-wave destruction method. J Appl Geophys. 2018;148:16-22. doi: 10.1016/j.jappgeo.2017.11.004

 

  1. Stockwell R, Mansinha L, Lowe R. Localization of the complex spectrum: the S transform. IEEE Trans Signal Process. 2002;44(4):998-1001. doi: 10.1109/78.492555

 

  1. Radad M, Gholami A, Siahkoohi HR. S-transform with maximum energy concentration: Application to non-stationary seismic deconvolution. J Appl Geophys. 2015;118:155-166. doi: 10.1016/j.jappgeo.2015.04.010

 

  1. Wang Q, Yang X, Tang B, Liu N, Gao J. Application of multi-synchrosqueezed generalized S-transform in seismic time-frequency analysis. J Seism Explor. 2023;32(1),39-49.

 

  1. Sun L, Chen X, Jiang W. Spectral analysis of seismic data based on a combined generalized S transform. J Appl Geophys. 2024;225:105383. doi: 10.1016/j.jappgeo.2024.105383

 

  1. Jiao W, Liu N, Jiang Y, Yang J, Zhao W. Unscaled generalized S-transform and its applications on seismic attenuation delineation: A case study in the Ordos Basin, Northwest China. J Seism Explor. 2026;35(1),237-250. doi: 10.36922/JSE025450106

 

  1. Liu N, Wei S, Zhang Z, et al. Frequency-corrected generalized S-transform and its application on prediction of reservoir oil-gas potential. Chin J Geophys. 2024;67(7):2820-2829. [In Chinese] doi: 10.6038/cjg2024R0579

 

  1. Silin D, Korneev V, Goloshubin G, Patzek T. Low-frequency asymptotic analysis of seismic reflection from a fluid-saturated medium. Transp Porous Media. 2006;62(3):283- 305. doi: 10.1007/s11242-005-0881-8

 

  1. Tian X, Liu G, Luo B, et al. Structural characteristics and their significance for hydrocarbon accumulation in the northern slope of the central Sichuan paleo-uplift. Acta Geol Sin-engl. 2022;96(4):1451-1470. doi: 10.1111/1755-6724.14975

 

  1. Yang S, Chen A, Zhang X, et al. Paleogeographic transition of the Permian Chihsia-Maokou period in the Sichuan Basin and indications for oil-gas exploration. Acta Sedimentol Sin. 2021;39(6):1466-1477. [In Chinese] doi: 10.14027/j.issn.1000-0550.2021.072

 

  1. Deng M, Zhao G, Lin X, Chen C, Li L, Liang Q. Sedimentary facies, paleogeography, and depositional models of the middle–late Permian in the sichuan basin, southwest China. Minerals. 2023;13(11):1406. doi: 10.3390/min13111406

 

  1. Borjigen T, Qin J, Fu X, Yang Y, Lu L. Marine hydrocarbon source rocks of the Upper Permian Longtan Formation and their contribution to gas accumulation in the northeastern Sichuan Basin, southwest China. Mar Pet Geoly. 2014;57:160- 172. doi: 10.1016/j.marpetgeo.2014.05.005

 

  1. Zhang Z, Liu N, Li S, et al. Gaussian-weighted multiple seismic attribute analysis for delineating subtle reservoirs: Application to the Maokou Formation, Central Sichuan Basin. Geophysics. 2026;91(1):IM23-IM38. doi: 10.1190/GEO-2025-0054
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept