1887
Volume 18 Number 1
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

Seismic velocity is an attractive parameter for geohazard interpretation, pore pressure analysis, play and prospect evaluations, and other geological studies, but ordinary seismic processing velocities often do not have a good enough resolution. We adapt a dynamic time warping algorithm to estimate geologically reasonable high‐resolution velocities from average‐quality seismic data that can be used for geohazard analysis based on 3D seismic data. To predict free gas and/or excess pore water pressure in thin shallow layers, we use velocity inversion. It is a method for simultaneous inversion of velocity data to geological attributes. It runs in the depth domain, uses a background velocity model for balancing of input velocities to wells and a normal compaction trend model to simultaneously estimate lithology, pore pressure and net apparent erosion attributes, while porosity is calculated from a sandstone–porosity relationship. The overall workflow is applied for geohazard analysis at two marine sites. The first example is a deep‐water one from the Norwegian Sea, where thin and possibly overpressured or gas‐filled layers are identified in the Pleistocene section. The second example is in a region with limestone‐dominated lithology, where thin overpressured shales can cause severe drilling problems. In both examples, the thicknesses of the layers prone to geohazards are estimated to be about half of the wavelength. Dedicated high‐resolution velocity estimations, such as those obtained through the proposed workflow with dynamic time warping, applied to standard 3D seismic data and followed by dedicated velocity inversion routines, are, therefore, a necessity for proper geohazard assessment.

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2020-01-07
2024-03-28
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References

  1. BarrettB.J., HuwsD.G., BoothA.D., WergelandO. and GreenJ.A.M.2017. Tuning, interference and false shallow gas signatures in geohazard interpretations: beyond the ‘λ/4’ rule. Near Surface Geophysics15, 359–366.
    [Google Scholar]
  2. BenfieldN., RambaranV., DowlathJ., SinclairT., EvansM., RichardsonJ.et al. 2017. Extracting geologic information directly from high‐resolution full‐waveform inversion velocity models — a case study from offshore Trinidad. The Leading Edge36, 67–74.
    [Google Scholar]
  3. BerndtD. and CliffordJ.1994. Using Dynamic Time Warping to Find Patterns in Time Series. AAAI‐94 Workshop on Knowledge Discovery in Databases (KDD‐94), Seattle, Washington.
    [Google Scholar]
  4. Biot M . 1941. General theory of three‐dimensional consolidation. Journal of Applied Physics12, 155–164.
    [Google Scholar]
  5. BowersG.2002. Detecting high overpressure. The Leading Edge21, 174–177.
    [Google Scholar]
  6. BulatJ. and LongD.2006. Use of 3D seismic data as a substitute for high‐resolution seismic surveys for site investigation. HSE Research Report 459.
  7. CaianiE., PortaA., BaselliG., TurielM., MuzzupappaS., PieruzziF.et al. 1998. Warped‐average template technique to track on a cycle‐by‐cycle basis the cardiac filling phases on left ventricular volume. IEEE Computers in Cardiology. Vol. 25, Cat. No.98CH36292, NY, USA.
  8. EatonB.A.1975. The equation for geopressure prediction from well logs. SPE 5544 (Society of Petroleum Engineers of AIME).
  9. GardnerG.H.F., GardnerL.W. and GregoryA.R.1974. Formation velocity and density – the diagnostic basics for stratigraphic traps. Geophysics39, 770–780.
    [Google Scholar]
  10. HottmanC.E. and JohnsonR.K.1965. Estimation of formation pressures from log‐derived shale properties. Journal of Petroleum Technology17, 717–722.
    [Google Scholar]
  11. KassarieK., MitchellS., AlbertinM., HillA. and CarneyR.2017. Identifying and mitigating against potential seafloor and shallow drilling hazards at a complex Gulf of Mexico Deepwater site using HR3D seismic and AUV data. Near Surface Geophysics15, 415–426.
    [Google Scholar]
  12. KeoghE. and PazzaniM.2000. Scaling up dynamic time warping for datamining applications. In 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston.
  13. KeoghE. and PazzaniM.2001. Derivative dynamic time warping. In First SIAM International Conference on Data Mining (SDM'2001), Chicago, USA.
  14. KtenasD., HenriksenE., MeisingsetI. and NielsenJ.K.2019. Estimation of net apparent erosion in the SW Barents Sea by applying VI analysis. Petroleum Geoscience25, 169–187.
    [Google Scholar]
  15. KtenasD., HenriksenE., MeisingsetI., NielsenJ.K. and AndreassenK.2017. Quantification of the magnitude of net erosion in the southwest Barents Sea using sonic velocities and compaction trends in shales and sandstones. Marine and Petroleum Geology88, 826–844.
    [Google Scholar]
  16. ManciniF., PrindleK., Ridsdill‐SmithT. and MossJ.2017. Full‐waveform inversion as a game changer: are we there yet?The Leading Edge 35445–448, 450–451.
    [Google Scholar]
  17. MeisingsetI., CokerJ. and LevenJ.2017. Geo‐pressure on the Australian North West Shelf. First EAGE workshop on pore pressure prediction. Extended Abstract.
    [Google Scholar]
  18. MeisingsetI., HubredJ.H. and KrasovaD.2018. High quality regional velocity modelling for depth conversion. First EAGE/PESGB workshop on velocities. Extended Abstract.
  19. MillerT.W., Luk, C.H. and OlgaardD.L.2004. The interrelationships between overpressure mechanisms and in‐situ stresses. AAPG Memoir 76, 13–20.
  20. NakagawaS. and NakanishiH.1988. Speaker‐independent English consonant and Japanese word recognition by a stochastic dynamic time warping method. IETE Journal of Research34, 87–89.
    [Google Scholar]
  21. OukiliJ., GruffeilleJ.P., OtterbeinC. and LoidlB.2019. Can high‐resolution reprocessed data replace the traditional 2D high‐resolution seismic data acquired for site surveys?First Break37, 49–54.
    [Google Scholar]
  22. PeikertE.W.1985. Stratigraphic velocity interpretation: national petroleum reserve – Alaska. In: Seismic Stratigraphy II: An Integrated Approach, AAPG Memoir, Vol. 39 (eds O.E.Berg and D.G.Wollwerton), pp. 209–224. American Association of Petroleum Geologists.
    [Google Scholar]
  23. RabinerL.R. and JuangB.H.1993. Fundamentals of Speech Recognition. RTP: Prentice Hall, Englewood Cliffs, NJ.
    [Google Scholar]
  24. RaymerL.L., HuntE.R. and GardnerJ.S.1980. An improved sonic transit time‐to‐porosity transform. Transactions of the SPWLA 21st Annual Logging Symposium, 8–11 July, Lafayette, Louisiana.
  25. SakoeH. and ChibaS.1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP‐26, 43–49.
  26. SalisburyR., CarringtonT., WoodG., BarwiseA., Van Der KraanM. and ThompsonG.2017. Guidance Notes for the Application of Geophysical and Geotechnical Techniques for Reducing Tophole Risks in the Drilling of Offshore Wells. The Society for Underwater Technology, London.
    [Google Scholar]
  27. SayersC.M., Den BoerL.D., NagyZ.R., HooymanP.J. and WardV.2005. Regional trends in undercompaction and overpressure in the Gulf of Mexico. SEG/Houston, USA, 2005 Annual Meeting.
  28. SayersC.M., JohnsonG.M. and DenyerG.2002. Predrill pore pressure prediction using seismic data. Geophysics67, 1286–1292.
    [Google Scholar]
  29. Schlumberger1989. Log Interpretation Charts, Chart POR‐3: Porosity Evaluation from Sonic. Schlumberger Educational Services, Houston, TX.
    [Google Scholar]
  30. SchmillM., Oates, T. and CohenP.1999. Learned models for continuous planning, in “The Seventh International Workshop on Artificial Intelligence and Statistics (AISTATS)”, 278–282.
  31. SilvaD.F. and BatistaG.E.A.P.A.2016. Speeding up all‐pairwise dynamic time warping matrix calculation. Proceedings of the 2016 SIAM International Conference on Data Mining, 837–845.
  32. TurhanT. and KoehlerF.1969. Velocity spectra‐digital computer derivation and application of velocity functions. Geophysics34, 859–881.
    [Google Scholar]
  33. YangkangC., TingtingL. and XiaohongC.2015. Velocity analysis using similarity‐weighted semblance. Geophysics80, 1JA–Z63.
    [Google Scholar]
  34. YiB., JagadishH. and FaloutsosC.1998. Efficient retrieval of similar time sequences under time warping, in “Proceedings of the 14th International Conference on Data Engineering (ICDE)”, IEEE Computer Society, Washington, DC, USA, 201–208.
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  • Article Type: Research Article
Keyword(s): 3D; Geohazard; Interpretation; Seismic; Shallow subsurface; Velocity

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