Skip to main content

Advertisement

Log in

Detecting Shallow Gas Reservoir in the F3 Block, the Netherlands, Using Offshore Seismic Data and High-Resolution Multi-Synchrosqueezing Transform

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

The presence of natural gas reserves is correlated frequently with the observation of a shadow with low frequency. The phenomenon known as "low-frequency shadow" describes a decrease in seismic frequencies measured from reflectors located immediately beneath a reservoir horizon. As a result of the non-stationary character of seismic signals, methods of time–frequency (TF) analysis are able to identify low-frequency shadows. This research introduces a recently created high-resolution TF transform that can evaluate a non-stationary signal in a stepwise reassignment operation while recovering the signal with sufficient accuracy. The proposed technique is termed "multi-synchrosqueezing transform (MSST)," which examines the signal iteratively using the synchrosqueezing transform (SST). To show the utility of the recommended TF analysis approach, we first described its algorithm through the use of various examples and then applied it to synthetic seismic data. The outcomes revealed that it could generate a sparser TF map than other conventional methods. To assess the effectiveness of the proposed MSST method on real seismic data and find shallow gas reservoirs, we applied it to 3D seismic data from the F3 block, which is located in the North Sea in the Netherlands. It is concluded that the proposed method has great potential to discover the precise location of a shallow gas reservoir.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26

Similar content being viewed by others

Data Availability

The real field dataset related to this paper can be found at https://terranubis.com/datainfo/F3-Demo-2020 which is an open-source seismic repository portal (Sciences, 1987).

References

  • Abdollahi Aghdam, B., & Ali Riahi, M. (2015). Application of modified AOGST to study the low frequency shadow zone in a gas reservoir. Journal of Geophysics and Engineering, 12(5), 770–779. https://doi.org/10.1088/1742-2132/12/5/770

    Article  Google Scholar 

  • Anvari, R., Siahsar, M. A. N., Gholtashi, S., Kahoo, A. R., & Mohammadi, M. (2017). Seismic random noise attenuation using synchrosqueezed wavelet transform and low-rank signal matrix approximation. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6574–6581.

    Article  Google Scholar 

  • Auger, F., & Flandrin, P. (1995). Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Transactions on signal processing, 43(5), 1068–1089.

    Article  Google Scholar 

  • Auger, F., Flandrin, P., Lin, Y.-T., McLaughlin, S., Meignen, S., Oberlin, T., & Wu, H.-T. (2013). Time-frequency reassignment and synchrosqueezing: An overview. IEEE Signal Processing Magazine, 30(6), 32–41.

    Article  Google Scholar 

  • Castagna, J. P., Sun, S., & Siegfried, R. W. (2003). Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The leading edge, 22(2), 120–127.

    Article  Google Scholar 

  • Chen, H., Lu, L., Xu, D., Kang, J., & Chen, X. (2017). The synchrosqueezing algorithm based on generalized S-transform for high-precision time-frequency analysis. Applied Sciences, 7(8), 769.

    Article  Google Scholar 

  • Chen, X., Chen, H., Fang, Y., & Hu, Y. (2020). High-order synchroextracting time–frequency analysis and its application in seismic hydrocarbon reservoir identification. IEEE Geoscience and Remote Sensing Letters, 18(11), 2011–2015.

    Article  Google Scholar 

  • Chen, H., Li, R., Hu, Y., & Fang, Y. (2022). Multisynchrosqueezing generalized s-transform and its application in tight sandstone gas reservoir identification. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  • Daubechies, I., Lu, J., & Wu, H.-T. (2011). Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and computational harmonic analysis, 30(2), 243–261.

    Article  Google Scholar 

  • De Jager, J. (2007). Geological development. Geology of the Netherlands, 5, 26.

    Google Scholar 

  • Drew, L. J., & Schuenemeyer, J. H. (1997). Oil-and gas-resource assessment in certain South American Basins—An application of ARDS (Ver. 5.0) to complex exploration and discovery histories. Nonrenewable Resources, 6, 295–315.

    Article  Google Scholar 

  • Duin, E., Doornenbal, J., Rijkers, R. H., Verbeek, J., & Wong, T. E. (2006). Subsurface structure of the Netherlands-results of recent onshore and offshore mapping. Netherlands Journal of Geosciences, 85(4), 245.

    Article  Google Scholar 

  • Dyman, T., Wyman, R., Kuuskraa, V., Lewan, M., & Cook, T. (2003). Deep natural gas resources. Natural Resources Research, 12, 41–56.

    Article  Google Scholar 

  • Ebrom, D. (2004). The low-frequency gas shadow on seismic sections. The leading edge, 23(8), 772–772.

    Article  Google Scholar 

  • Fang, Y., Chen, H., Hu, Y., Li, R., & Li, J. (2021a). Application of adaptive parameterized S-transform to delta sandstone reservoir identification. Geophysical Prospecting, 69(8–9), 1689–1699.

    Article  Google Scholar 

  • Fang, Y., Hu, Y., Li, M., Chen, H., Chen, X., & Li, J. (2021b). Second-order horizontal multi-synchrosqueezing transform for hydrocarbon reservoir identification. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  • Gholtashi, S., Nazari Siahsar, M. A., RoshandelKahoo, A., Marvi, H., & Ahmadifard, A. (2015). Synchrosqueezing-based transform and its application in seismic data analysis. Iranian Journal of Oil and Gas Science and Technology, 4(4), 1–14.

    Google Scholar 

  • Goloshubin, G., Van Schuyver, C., Korneev, V., Silin, D., & Vingalov, V. (2006). Reservoir imaging using low frequencies of seismic reflections. The leading edge, 25(5), 527–531.

    Article  Google Scholar 

  • Guo, Q., Islam, N., & Pennington, W. D. (2014). Tuning, AVO, and flatspot effects in North Sea Block F3. In SEG Technical Program Expanded Abstracts 2014 (pp. 538-542). Society of Exploration Geophysicists. https://doi.org/10.1190/segam2014-1392.1

  • Hamidi, M., Hosseini, S. K., & Sadeghi, H. (2011). Successful application s-transform time-frequency method in hydrocarbon reservoirs and low frequency shadows detection. Proceedings of the 10th SEGJ International Symposium,

  • Han, L., Bonar, D., & Sacchi, M. (2012). Seismic denoising by time-frequency reassignment. CSEG Expanded Abstracts.

  • Han, L., Sacchi, M. D., & Han, L. (2014). Spectral decomposition and de-noising via time-frequency and space-wavenumber reassignment. Geophysical Prospecting, 62(2), 244–257.

    Article  Google Scholar 

  • He, Y., Zhu, J., Zhang, Y., Liu, A., & Pan, G. (2017). The Research and Application of Bright Spot Quantitative Interpretation in Deepwater Exploration. Open Journal of Geology, 7(4), 588–601.

    Article  Google Scholar 

  • Herrera, R. H., Han, J., & van der Baan, M. (2014). Applications of the synchrosqueezing transform in seismic time-frequency analysis. Geophysics, 79(3), V55–V64.

    Article  Google Scholar 

  • Hu, Y., Chen, H., Qian, H., Zhou, X., Wang, Y., & Lyu, B. (2020). A high-precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S-transform. Geophysical Prospecting, 68(3), 941–954.

    Article  Google Scholar 

  • Huang, Z.-L., Zhang, J., Zhao, T.-H., & Sun, Y. (2015). Synchrosqueezing S-transform and its application in seismic spectral decomposition. IEEE Transactions on Geoscience and Remote Sensing, 54(2), 817–825.

    Article  Google Scholar 

  • Ishak, M. A., Islam, M. A., Shalaby, M. R., & Hasan, N. (2018). The application of seismic attributes and wheeler transformations for the geomorphological interpretation of stratigraphic surfaces: a case study of the f3 block, Dutch offshore sector, north sea. Geosciences, 8(3), 79.

    Article  Google Scholar 

  • Kushwaha, P. K., Maurya, S., Rai, P., & Singh, N. (2020). Porosity prediction from offshore seismic data of F3 Block, the Netherlands using multi-layer feed-forward neural network. CURRENT SCIENCE, 119(10), 1652.

    Article  Google Scholar 

  • Lari, H. H., Naghizadeh, M., Sacchi, M. D., & Gholami, A. (2019). Adaptive singular spectrum analysis for seismic denoising and interpolation. Geophysics, 84(2), V133–V142.

    Article  Google Scholar 

  • Li, R., Chen, H., Fang, Y., Hu, Y., Chen, X., & Li, J. (2021a). Synchrosqueezing polynomial chirplet transform and its application in tight sandstone gas reservoir identification. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  • Li, R., Zhu, X., Zhou, Y., Chen, H., Chen, X., & Hu, Y. (2021b). Generalized W Transform and Its Application in Gas-Bearing Reservoir Characterization. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  • Li, S., & Rao, Y. (2020). Poroelastic property analysis of seismic low-frequency shadows associated with gas reservoirs. Journal of Geophysics and Engineering, 17(3), 463–474.

    Article  Google Scholar 

  • Li, Z., Sun, F., Gao, J., Liu, N., & Wang, Z. (2021c). Multi-synchrosqueezing wavelet transform for time–frequency localization of reservoir characterization in seismic data. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  • Li, Z., Wang, P., Wang, D., Li, Z., Sun, M., He, Z., & Ding, Y. (2020). Hydrocarbon identification based on bright spot technique by using matching pursuit and RGB blending. IEEE Access, 8, 184731–184743.

    Article  Google Scholar 

  • Lin, Y., Chen, S., Zhang, G., Huang, M., & Wang, B. (2022). High-resolution time–frequency analysis based on a synchroextracting adaptive S-transform and its application. Journal of Geophysics and Engineering, 19(5), 1124–1133.

    Article  Google Scholar 

  • Liu, G., Fomel, S., & Chen, X. (2011). Time-frequency analysis of seismic data using local attributes. Geophysics, 76(6), P23–P34.

    Article  Google Scholar 

  • Liu, J. (2006). Spectral decomposition and its application in mapping stratigraphy and hydrocarbons [Ph. D. thesis]. Houston, TX, USA: University of Houston.

  • Liu, N., Gao, J., Jiang, X., Zhang, Z., & Wang, Q. (2016). Seismic time–frequency analysis via STFT-based concentration of frequency and time. IEEE Geoscience and Remote Sensing Letters, 14(1), 127–131.

    Article  Google Scholar 

  • Liu, W., Cao, S., & Chen, Y. (2015). Seismic time–frequency analysis via empirical wavelet transform. IEEE Geoscience and Remote Sensing Letters, 13(1), 28–32.

    Article  Google Scholar 

  • Liu, W., Cao, S., Wang, Z., Jiang, K., Zhang, Q., & Chen, Y. (2018). A novel approach for seismic time-frequency analysis based on high-order synchrosqueezing transform. IEEE Geoscience and Remote Sensing Letters, 15(8), 1159–1163.

    Article  Google Scholar 

  • Mahdavi, A., Kahoo, A. R., Radad, M., & Monfared, M. S. (2021). Application of the local maximum synchrosqueezing transform for seismic data. Digital Signal Processing, 110, 102934.

    Article  Google Scholar 

  • Moosavi, V., Mahjoobi, J., & Hayatzadeh, M. (2021). Combining group method of data handling with signal processing approaches to improve accuracy of groundwater level modeling. Natural Resources Research, 30, 1735–1754.

    Article  Google Scholar 

  • Naseer, M. T. (2021). Imaging of Stratigraphic Pinch-Out Traps Within the Lower-Cretaceous Shaly-Sandstone System, Pakistan, Using 3D Quantitative Seismic Inverted Porosity-Velocity Modeling. Natural Resources Research, 30(6), 4297–4327.

    Article  Google Scholar 

  • Nazari Siahsar, M. A., Gholtashi, S., Roshandel Kahoo, A., Marvi, H., & Ahmadifard, A. (2016). Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition. Geophysics, 81(2), V117–V124.

    Article  Google Scholar 

  • Nikoo, A., Roshandel Kahoo, A., Hassanpour, H., & Saadatnia, H. (2016). Using a time-frequency distribution to identify buried channels in reflection seismic data. Digital Signal Processing, 54, 54–63.

    Article  Google Scholar 

  • Pegrum, R., & Spencer, A. (1990). Hydrocarbon plays in the northern North Sea. Geological Society, London, Special Publications, 50(1), 441–470.

    Article  Google Scholar 

  • Pham, D.-H., & Meignen, S. (2017). High-order synchrosqueezing transform for multicomponent signals analysis—With an application to gravitational-wave signal. IEEE Transactions on signal processing, 65(12), 3168–3178.

    Article  Google Scholar 

  • Radad, M. (2018). Application of Single-Frequency Time-Space Filtering Technique for Seismic Ground Roll and Random Noise Attenuation. Journal of the Earth and Space Physics, 44(4), 41–51.

    Google Scholar 

  • Radad, M. (2020). Time-frequency analysis of seismic data by reassigned S-transform to detect low frequency shadows (in Persian). Journal of research on applied geophysics, 5(2), 283–293.

    Google Scholar 

  • Radad, M., Gholami, A., & Siahkoohi, H. R. (2015). S-transform with maximum energy concentration: Application to non-stationary seismic deconvolution. Journal of Applied Geophysics, 118, 155–166.

    Article  Google Scholar 

  • Radad, M., Gholami, A., & Siahkoohi, H. R. (2016). A fast method for generating high-resolution single-frequency seismic attributes. Journal of Seismic Exploration, 25(1), 11–25.

    Google Scholar 

  • Roshandel Kahoo, A., & Nejati Kalateh, A. (2012). High resolution spectral decomposition and its application in the illumination of low-frequency shadows of a gas reservoir (in Persian). Iranian Journal of Geophysics, 6(1), 61–68.

    Google Scholar 

  • Roshandel Kahoo, A., & Siahkoohi, H. (2009a). Gas detection from AVO analysis in time-frequency domain. 71st EAGE Conference and Exhibition incorporating SPE EUROPEC,

  • Roshandel Kahoo, A., & Siahkoohi, H. (2009b). Random noise suppression from seismic data using time-frequency peak filtering. 71st EAGE Conference and Exhibition incorporating SPE EUROPEC,

  • Roshandel Kahoo, A., & SiahKoohi, H. R. (2010). Seismic attenuation coefficient estimation using smoothed pseudo Wigner-Ville distribution (in Persian). Journal of the Earth and Space Physics, 36(3).

  • SALES, J. K. (1992). Uplift and subsidence do northwestern Europe: Possible causes and influence on hydrocarbon productivity. Norsk geologisk tidsskrift, 72(3), 253–258.

    Google Scholar 

  • Schroot, B., & De Haan, H. (2003). An improved regional structural model of the Upper Carboniferous of the Cleaver Bank High based on 3D seismic interpretation. Geological Society, London, Special Publications, 212(1), 23–37.

    Article  Google Scholar 

  • Sciences, d. E. (1987). The Netherlands Offshore, The North Sea, F3 Block—Complete. In: dGB Earth Sciences.

  • Shirazi, M., Kahoo, A. R., & Chen, Y. (2018). Detection of Low-frequency Shadows Associated with Gas Using High-resolution Empirical Wavelet Transform. 80th EAGE Conference and Exhibition 2018,

  • Sinha, S., Routh, P. S., Anno, P. D., & Castagna, J. P. (2005). Spectral decomposition of seismic data with continuous-wavelet transform. Geophysics, 70(6), P19–P25.

    Article  Google Scholar 

  • Sun, S., Castagna, J. P., & Siegfried, R. W. (2002). Examples of wavelet transform time-frequency analysis in direct hydrocarbon detection. In SEG Technical Program Expanded Abstracts 2002 (pp. 457-460). Society of Exploration Geophysicists. https://doi.org/10.1190/1.1817281

  • Taner, M. T., Koehler, F., & Sheriff, R. (1979). Complex seismic trace analysis. Geophysics, 44(6), 1041–1063.

    Article  Google Scholar 

  • Thakur, G., & Wu, H.-T. (2011). Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples. SIAM Journal on Mathematical Analysis, 43(5), 2078–2095.

    Article  Google Scholar 

  • Tian, Y., Gao, J., & Wang, D. (2021). The multisynchrosqueezing optimal basic wavelet transform and applications to sedimentary cycle division. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  • Wang, P., Gao, J., & Wang, Z. (2014). Time-frequency analysis of seismic data using synchrosqueezing transform. IEEE Geoscience and Remote Sensing Letters, 11(12), 2042–2044.

    Article  Google Scholar 

  • Wang, Q., Gao, J., & Liu, N. (2019). Second-order synchrosqueezing wave packet transform and its application for characterizing seismic geological structures. IEEE Geoscience and Remote Sensing Letters, 17(5), 760–764.

    Article  Google Scholar 

  • Wang, S., Chen, X., Cai, G., Chen, B., Li, X., & He, Z. (2013). Matching demodulation transform and synchrosqueezing in time-frequency analysis. IEEE Transactions on signal processing, 62(1), 69–84.

    Article  Google Scholar 

  • Wang, S., Chen, X., Selesnick, I. W., Guo, Y., Tong, C., & Zhang, X. (2018). Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis. Mechanical Systems and Signal Processing, 100, 242–288.

    Article  Google Scholar 

  • Wang, Y. (2007). Seismic time-frequency spectral decomposition by matching pursuit. Geophysics, 72(1), V13–V20.

    Article  Google Scholar 

  • Wang, Y. (2010). Multichannel matching pursuit for seismic trace decomposition. Geophysics, 75(4), V61–V66.

    Article  Google Scholar 

  • Wei, D., Huang, K., Huang, H., Wang, B., Ao, J., Deng, L., & Peng, J. (2023). Local maximum multi-synchrosqueezing transform for the analysis of time-varying signals. Journal of Physics: Conference Series,

  • Wu, X., & Liu, T. (2009). Spectral decomposition of seismic data with reassigned smoothed pseudo Wigner-Ville distribution. Journal of Applied Geophysics, 68(3), 386–393.

    Article  Google Scholar 

  • Wu, X., & Liu, T. (2010). Seismic spectral decomposition and analysis based on Wigner-Ville distribution for sandstone reservoir characterization in West Sichuan depression. Journal of Geophysics and Engineering, 7(2), 126–134.

    Article  Google Scholar 

  • Xue, Y.-J., Cao, J.-X., & Tian, R.-F. (2013). A comparative study on hydrocarbon detection using three EMD-based time–frequency analysis methods. Journal of Applied Geophysics, 89, 108–115.

    Article  Google Scholar 

  • Xue, Y.-J., Cao, J.-X., Tian, R.-F., Du, H.-K., & Shu, Y.-X. (2014). Application of the empirical mode decomposition and wavelet transform to seismic reflection frequency attenuation analysis. Journal of Petroleum Science and Engineering, 122, 360–370.

    Article  Google Scholar 

  • Yu, G. (2018). Demodulated synchrosqueezing transform. Chinese Automation Congress (CAC),

  • Yu, G., Wang, Z., & Zhao, P. (2018). Multisynchrosqueezing transform. IEEE Transactions on Industrial Electronics, 66(7), 5441–5455.

    Article  Google Scholar 

  • Yu, G., Wang, Z., Zhao, P., & Li, Z. (2019). Local maximum synchrosqueezing transform: an energy-concentrated time-frequency analysis tool. Mechanical Systems and Signal Processing, 117, 537–552.

    Article  Google Scholar 

  • Yu, G., Yu, M., & Xu, C. (2017). Synchroextracting transform. IEEE Transactions on Industrial Electronics, 64(10), 8042–8054.

    Article  Google Scholar 

  • Zhang, G., Duan, J., Li, Y., He, C., Du, H., Luo, F., Zhan, Y., & Wang, J. (2020). Adaptive time-resampled high-resolution synchrosqueezing transform and its application in seismic data. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6691–6698.

    Article  Google Scholar 

  • Zhu, X., Zhang, Z., Li, Z., Gao, J., Huang, X., & Wen, G. (2019). Multiple squeezes from adaptive chirplet transform. Signal Processing, 163, 26–40.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amin Roshandel Kahoo.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shirazi, M., Roshandel Kahoo, A., Radad, M. et al. Detecting Shallow Gas Reservoir in the F3 Block, the Netherlands, Using Offshore Seismic Data and High-Resolution Multi-Synchrosqueezing Transform. Nat Resour Res 32, 2007–2035 (2023). https://doi.org/10.1007/s11053-023-10229-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-023-10229-w

Keywords

Navigation