1887
Volume 71, Issue 2
  • E-ISSN: 1365-2478
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Abstract

Abstract

A Bayesian inversion methodology is proposed that inverts angle‐stacked 4D seismic maps to changes in pressure, water saturation and gas saturation. The inversion method is applied to data from a siliciclastic reservoir in the west of Shetlands, UK continental shelf. We present inversion results for three seismic monitor surveys and demonstrate the added value of pressure‐saturation inversion by providing insights into reservoir connectivity and fluid dynamics across 14 years of reservoir production. In these surveys, 4D seismic signals related to waterflood, pressure increase and depletion, and gas exsolution are evident and overlap each other in many regions. To regularize this ill‐posed inversion problem, we propose a Bayesian formulation that incorporates spatially variant prior information derived from a history‐matched reservoir simulation model and well pressure measurements. The benefit of incorporating these multi‐disciplinary data as prior information is demonstrated by comparing to inversion results using a spatially invariant prior. We show that the method takes advantage of the multi‐disciplinary prior information to make more precise inferences where the seismic data are most uncertain. This leads to more realistic spatial distributions for the pressure and water‐saturation inversion results. The non‐uniqueness in this non‐linear inversion is studied by analysing uncertainty estimates produced by stochastic sampling of the Bayesian posterior distribution. Posterior standard deviations are observed to be related to the sensitivity of the seismic amplitudes to the changes in each dynamic property as well as the degree of overlap between changes in different dynamic properties. Estimated pressure increases have a posterior standard deviation of approximately 1 MPa, whereas posterior standard deviations for pressure decrease are on average 4 MPa, with higher values applicable to regions of gas exsolution. The posterior standard deviation for estimates of gas saturation change is 0.07 for moderate, visible saturation signals, but 0.005 for low‐to‐zero gas saturation change. The posterior standard deviation for estimated water saturation change is mainly influenced by overlapping changes in pressure and gas saturation. When water changes dominate the 4D seismic signal, posterior standard deviations are on average 0.05. These values rise to 0.25 in areas where the water change is obscured by pressure or gas‐saturation changes.

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2023-01-20
2024-04-25
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References

  1. Aki, K. & Richards, P. (1980) Quantitative seismology: theory and methods, vol. 1, W.H. Freeman and Co, San Francisco.
  2. Alvarez, E. & MacBeth, C. (2014) An insightful parametrization for the flatlander's interpretation of time‐lapsed seismic data. Geophysical Prospecting, 62, 75–96. https://doi.org/10.1111/1365‐2478.12071
    [Google Scholar]
  3. Amini, H. (2014) A pragmatic approach to simulator‐to‐seismic modelling for 4D seismic interpretation. PhD Thesis. Heriot‐Watt University.
    [Google Scholar]
  4. Amini, H. (2018a) Calibration of minerals’ and dry rock elastic moduli in sand‐shale mixtures. In: 80th EAGE conference & exhibition, Copenhagen, Denmark. Extended Abstracts.
  5. Amini, H. (2018b) Comparison of Xu‐White, Simplified Xu‐White (Keys & Xu) and Nur ’ s critical porosity in shaley sands. In: 80th EAGE conference & exhibition, Copenhagen, Denmark. Extended Abstracts.
  6. Amini, H., MacBeth, C. & Shams, A. (2020) Seismic modelling for reservoir studies: a comparison between convolutional and full‐waveform methods for a deep‐water turbidite sandstone reservoir. Geophysical Prospecting, 68, 1540–1553. https://doi.org/10.1111/1365‐2478.12936
    [Google Scholar]
  7. Amini, H. & MacBeth, C. (2015) Calibration of rock stress‐sensitivity using 4D seismic data. In: 77th EAGE annual meeting, Madrid, Spain. Extended Abstracts, Th P6 14.
  8. Angelov, P., Spetzler, J. & Wapenaar, K. (2004) Pore pressure and water saturation variations; modification of Landrø’s AVO approach. In: SEG technical program expanded abstracts 2004. pp. 2279–2282. https://doi.org/10.1190/1.1851220
  9. Batzle, M. & Wang, Z. (1992) Seismic properties of pore fluids. Geophysics, 57, 1396–1408. https://doi.org/10.1190/1.1443207
    [Google Scholar]
  10. Bhakta, T. (2018) Improvement of pressure‐saturation changes estimations from time‐lapse PP‐AVO data by using non‐linear optimization method. Journal of Applied Geophysics, 155, 1–12. https://doi.org/10.1016/j.jappgeo.2018.04.020
    [Google Scholar]
  11. Bhakta, T. & Landrø, M. (2013) Estimation of pressure‐saturation changes for unconsolidated reservoir rocks with high VP/VS ratio. Geophysics, 79, M35–M54. https://doi.org/10.1190/GEO2013‐0434.1
    [Google Scholar]
  12. Campbell, S., Schons, M., Mathew, S., Khalil, A., Riley, D., Hill, C. et al. (2015) Optimising value through improved 4D seismic processing on 10 vintages – Foinaven‐Schiehallion‐Loyal Case History. In: 77th EAGE conference & exhibition, Madrid, Spain. pp. 1–4.
  13. Chib, S. & Greenberg, E. (1995) Understanding the Metropolis‐Hastings algorithm. The American Statistician, 49, 327–335. https://doi.org/10.1080/00031305.1995.10476177
    [Google Scholar]
  14. Coleou, T., van Wijngaarden, A.J., Norenes Haaland, A., Moliere, P., Ona, R. & Formento, J.L. (2007) Petrophysical seismic inversion for porosity and 4D calibration on the troll field. In: 69th EAGE conference and exhibition incorporating SPE EUROPEC>. pp. 11–14. https://doi.org/10.3997/2214‐4609.201401435
  15. Coleou, T., Roustiau, A., Machecler, I., Ayzenberg, M., Fayemendy, C., Skjei, N. et al. (2013) 4D petrophysical seismic inversion – case studies. In: 75th EAGE annual meeting, London, UK. Extended Abstracts, We 17 03. pp. 10–13.
  16. Correia, G.G., Davolio, A. & Schiozer, D.J. (2014) Improvement of pressure and saturation estimations from 4D seismic supported by flow simulation data. In: 76th EAGE conference & exhibition 2014. p. 4. https://doi.org/10.3997/2214‐4609.20141145
  17. Côrte, G., Chassagne, R. & MacBeth, C. (2021) Seismic history matching in the pressure and saturation domain for reservoir connectivity assessment. In: 82nd EAGE annual conference & exhibition. pp. 1–5. https://doi.org/10.3997/2214‐4609.202112956
  18. Côrte, G. (2020) Development of techniques for quantifying pressure and saturation changes from 4D seismic data applied to a North Sea field. PhD Thesis. Heriot‐Watt University.
    [Google Scholar]
  19. Dadashpour, M., Landrø, M. & Kleppe, J. (2008) Nonlinear inversion for estimating reservoir parameters from time‐lapse seismic data. Journal of Geophysics and Engineering, 5, 54–66. https://doi.org/10.1088/1742‐2132/5/1/006
    [Google Scholar]
  20. Davolio, A., Maschio, C. & Schiozer, D.J. (2012) Pressure and saturation estimation from P and S impedances: a theoretical study. Journal of Geophysics and Engineering, 9, 447–460. https://doi.org/10.1088/1742‐2132/9/5/447
    [Google Scholar]
  21. Davolio, A., Maschio, C. & Schiozer, D. (2013) A methodology to constrain pressure and saturation estimation from 4D seismic using multiple simulation models and observed data. Journal of Petroleum Science and Engineering, 105, 51–61.
    [Google Scholar]
  22. Domenico, S.N. (1974) Effect of water saturation on seismic reflectivity of sand reservoirs encased in shale. Geophysics, 39, 759–769. https://doi.org/10.1190/1.1440464
    [Google Scholar]
  23. Dyce, M., Whitcombe, D., McKenzie, C. & Hodgson, L. (2004) The quantification of 4D noise. In: 66th EAGE conference & exhibition. cp‐133‐00047. https://doi.org/10.3997/2214‐4609.201405653
  24. El Ouair, Y., Buland, A., Osdal, B. & Furre, A.‐K. (2005) Improving drainage interpretation using a new Bayesian time‐lapse inversion. In: 67th EAGE conference & exhibition. pp. 13–16. https://doi.org/10.3997/2214‐4609‐pdb.1.C019
  25. Emerick, A. (2014) Estimation of pressure and saturation fields from time‐lapse impedance data using the ensemble smoother. Journal of Geophysics and Engineering, 11, 035007. https://doi.org/10.1088/1742‐2132/11/3/035007
    [Google Scholar]
  26. Floricich, M. (2006) An engineering‐consistent approach for pressure and saturation estimation from time‐lapse seismic data. PhD Thesis. Heriot‐Watt University.
  27. Floricich, M., MacBeth, C. & Staples, R. (2005) An engineering‐driven approach for separating pressure and saturation using 4D seismic: application to a Jurassic reservoir in the UK North Sea. In: 75th SEG international exposition and annual meeting. pp. 2464–2467. https://doi.org/10.1190/1.2148221
  28. Floricich, M., MacBeth, C., Stammeijer, J., Staples, R., Evans, A. & Dijksman, C. (2006) A new technique for pressure – saturation separation from time‐lapse seismic – Schiehallion case study. In: 68th EAGE conference and exhibition incorporating SPE EUROPEC 2006. pp. 1459–1463. https://doi.org/10.3997/2214‐4609.201402389
  29. Floricich, M., Jenkins, G. & McCormick, D. (2012) Probabilistic inversion of multiple 4D seismic as applied on Schiehallion field. In: 74th EAGE conference & exhibition incorporating SPE EUROPEC 2012, 4–7 June 2012, Copenhagen, Denmark. pp. 4–7.
  30. Gainski, M., MacGregor, A.G., Freeman, P.J. & Nieuwland, H.F. (2010) Turbidite reservoir compartmentalization and well targeting with 4D seismic and production data: Schiehallion field, UK. Geological Society Special Publication, 347, 89–102. https://doi.org/10.1144/SP347.7
    [Google Scholar]
  31. Gassmann, F. (1951) Elastic waves through a packing of spheres. Geophysics, 16, 673–685. https://doi.org/10.1190/1.1437718
    [Google Scholar]
  32. Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013) Bayesian data analysis, 3rd edition, Chapman & Hall/CRC Texts in Statistical Science Series.
    [Google Scholar]
  33. Gjerding, K.G., Skjei, N.S., Norenes Haaland, A.N.H., Riste, P.R., Coléou, T.C. & Machecler, I.M. (2008) 4‐D petrophysical seismic inversion on the troll west field. In: 70th EAGE conference and exhibition incorporating SPE EUROPEC 2008. pp. 1922–1926. https://doi.org/10.3997/2214‐4609.20147814
  34. Govan, A., Primmer, T., Douglas, C., Moodie, N., Davies, M. & Nieuwland, F. (2006) Reservoir management in a deepwater subsea field – the Schiehallion experience. SPE Reservoir Evaluation and Engineering, 9, 382–390.
    [Google Scholar]
  35. Grana, D. (2016) Bayesian linearized rock‐physics inversion. Geophysics, 81, D625–D641. https://doi.org/10.1190/GEO2016‐0161.1
    [Google Scholar]
  36. Grana, D. & Della Rossa, E. (2010) Probabilistic petrophysical‐properties estimation integrating statistical rock physics with seismic inversion. Geophysics, 75(3), 1MJ–Z72. https://doi.org/10.1190/1.3386676
    [Google Scholar]
  37. Grana, D. & Mukerji, T. (2015) Bayesian inversion of time‐lapse seismic data for the estimation of static reservoir properties and dynamic property changes. Geophysical Prospecting, 63, 637–655. https://doi.org/10.1111/1365‐2478.12203
    [Google Scholar]
  38. Grude, S., Landrø, M. & Osdal, B. (2013) Time‐lapse pressure‐saturation discrimination for CO2 storage at the Snøhvit field. International Journal of Greenhouse Gas Control, 19, 369–378. https://doi.org/10.1016/j.ijggc.2013.09.014
    [Google Scholar]
  39. Hastings, W.K. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109. https://doi.org/10.1093/biomet/57.1.97
    [Google Scholar]
  40. Hill, R. (1963) Elastic properties of reinforced solids: some theoretical principles. Journal of the Mechanics and Physics of Solids, 11, 357–372. https://doi.org/10.1016/0022‐5096(63)90036‐X
    [Google Scholar]
  41. Kragh, E. & Christie, P. (2002) Seismic repeatability, normalized RMS, and predictability. The Leading Edge, 21, 640–647. https://doi.org/10.1190/1.1497316
    [Google Scholar]
  42. Kvam, Ø. & Landrø, M. (2005) Pore‐pressure detection sensitivities tested with time‐lapse seismic data. Geophysics, 70, 39–50. https://doi.org/10.1190/1.2122416
    [Google Scholar]
  43. Lamers, E. & Carmichael, S.M.M. (1999) The Paleocene deepwater sandstone play West of Shetland. Petroleum Geology Conference Proceedings, 5, 645–659. https://doi.org/10.1144/0050645
    [Google Scholar]
  44. Landa, J., Meadows, M., Thacher, C., Waddle, R. & Williams, N. (2015) Map‐based estimation of reservoir pressure and saturation from 4D seismic with a data‐driven procedure approaches for pressure‐saturation inversion from 4D seismic model‐driven inversion approach. In: SPE annual technical conference and exhibition, Houston, Texas, USA.
  45. Landrø, M. (2001) Discrimination between pressure and fluid saturation changes from time lapse seismic data. Geophysics, 66, 836–844.
    [Google Scholar]
  46. Landrø, M., Veire, H.H., Duffaut, K. & Najjar, N. (2003) Discrimination between pressure and fluid saturation changes from marine multicomponent time‐lapse seismic data. Geophysics, 68, 1592–1599. https://doi.org/10.1190/1.1620633
    [Google Scholar]
  47. Lang, X. & Grana, D. (2019) Rock physics modelling and inversion for saturation‐pressure changes in time‐lapse seismic studies. Geophysical Prospecting, 67, 1912–1928. https://doi.org/10.1111/1365‐2478.12797
    [Google Scholar]
  48. Leach, H.M., Herbert, N., Los, A. & Smith, R.L. (1999) The Schiehallion development. In: Petroleum geology conference series, vol. 5. London: Geological Society. pp. 683–692. https://doi.org/10.1144/0050683
  49. Li, X.P., Hodges, E., Widmaier, M., Sundvor, E. & Eidsvig, S. (2004) Oseberg 4D re‐processing – a case history of seismic repeatability analysis. In: SEG technical program expanded abstracts 2004. SEG Technical Program Expanded Abstracts. pp. 2223–2226. https://doi.org/10.1190/1.1851213
  50. Lumley, D., Adams, D.C., Meadows, M., Cole, S. & Wright, R. (2003) 4D seismic data processing issues and examples. SEG Technical Program Expanded Abstracts, 22, 1394–1397. https://doi.org/10.1190/1.1817550
    [Google Scholar]
  51. MacBeth, C. (2004) A classification for the pressure‐sensitivity properties of a sandstone rock frame. Geophysics, 69, 497–510.
    [Google Scholar]
  52. MacBeth, C., Floricich, M. & Soldo, J. (2006) Going quantitative with 4D seismic analysis. Geophysical Prospecting, 54, 303–317. https://doi.org/10.1111/j.1365‐2478.2006.00536.x
    [Google Scholar]
  53. Mavko, G., Mukerji, T. & Dvorkin, J. (2009) The rock physics handbook. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511626753
    [Google Scholar]
  54. Meadows, M.A. (2001) Enhancements to Landro's method for separating time‐lapse pressure and saturation changes. In: SEG technical program expanded abstracts 2001. SEG Technical Program Expanded Abstracts. pp. 1652–1655. https://doi.org/10.1190/1.1816433
  55. Meadows, M.A. & Cole, S.P. (2013) 4D seismic modeling and CO2 pressure‐saturation inversion at the Weyburn Field. Saskatchewan. International Journal of Greenhouse Gas Control, 16, S103–S117. https://doi.org/10.1016/j.ijggc.2013.01.030
    [Google Scholar]
  56. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. & Teller, E. (1953) Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21, 1087–1092. https://doi.org/10.1063/1.1699114
    [Google Scholar]
  57. Mindlin, R.D. (1949) Compliance of elastic bodies in contact. Journal of Applied Mechanics, 16, 259–268. https://doi.org/10.1115/1.4009973
    [Google Scholar]
  58. Nur, A., Mavko, G., Dvorkin, J. & Galmudi, D. (1998) Critical porosity: a key to relating physical properties to porosity in rocks. The Leading Edge, 17, 357–362.
    [Google Scholar]
  59. Obidegwu, D., Chassagne, R., & MacBeth, C. (2017). Seismic assisted history matching using binary maps. Journal of Natural Gas Science and Engineering, 42, 69–84. https://doi.org/10.1016/j.jngse.2017.03.001
    [Google Scholar]
  60. Omofoma, V. (2017) The quantification of pressure and saturation changes in clastic reservoirs using 4D seismic data. PhD Thesis. Heriot‐Watt University.
    [Google Scholar]
  61. Pickup, G.E., Kiatsakulphan, M. & Mills, J.R. (2010) Analysis of grid resolution for simulations of CO2 storage in deep saline aquifers. In: 12th EAGE mathematics of oil recovery European conference [ECMOR] proceedings. https://doi.org/10.3997/2214‐4609.20144939
  62. Ribeiro, C. & MacBeth, C. (2006) Time‐lapse seismic inversion for pressure and saturation in Foinaven field, west of Shetland. First Break, 24, 63–72.
    [Google Scholar]
  63. Richardson, S.M., Herbert, N. & Leach, H.M. (1997) How well connected is the Schiehallion reservoir? In: SPE offshore Europe, Aberdeen. https://doi.org/10.2118/38560‐MS
  64. Shahin, A., Stoffa, P.L., Tatham, R.H. & Sava, D. (2011) Multi‐component time‐lapse seismic: on saturation‐pressure discrimination and statistical detectability of fluid flow. Journal of Seismic Exploration, 20, 357–378.
    [Google Scholar]
  65. Smith, G.C. & Gidlow, P.M. (1987) Weighted stacking for rock property estimation and detection of gas. Geophysical Prospecting, 35, 993–1014.
    [Google Scholar]
  66. Stephen, K.D. & MacBeth, C. (2006) Seismic history matching in the UKCS Schiehallion field. First Break, 24, 43–49. https://doi.org/10.3997/1365‐2397.2006009
    [Google Scholar]
  67. Stovas, A. & Landrø, M. (2004) Optimal use of PP and PS time‐lapse stacks for fluid‐pressure discrimination. Geophysical Prospecting, 52, 301–312. https://doi.org/10.1111/j.1365‐2478.2004.00420.x
    [Google Scholar]
  68. Stovas, A., Landrø, M. & Arntsen, B. (2006) A sensitivity study based on 2D synthetic data from the Gullfaks Field, using PP and PS time‐lapse stacks for fluid‐pressure discrimination. Journal of Geophysics and Engineering, 3, 314–328. https://doi.org/10.1088/1742‐2132/3/4/003
    [Google Scholar]
  69. Trani, M., Arts, R., Leeuwenburgh, O. & Brouwer, J. (2011) Estimation of changes in saturation and pressure from 4D seismic AVO and time‐shift analysis. Geophysics, 76, C1–C17.
    [Google Scholar]
  70. Veire, H.H., Borgos, H.G. & Landrø, M. (2006) Stochastic inversion of pressure and saturation changes from time‐lapse AVO data. Geophysics, 71, C81–C92. https://doi.org/10.1190/1.2235858
    [Google Scholar]
  71. Wong, M.Y., JafarGandomi, A., MacBeth, C., Bertrand, A. & Hoeber, H. (2015) Pressure and saturation change inversion using 4D seismic: application to a chalk reservoir in North Sea. In: SEG technical program expanded abstracts, vol. 34. pp. 5430–5434. https://doi.org/10.1190/segam2015‐5813716.1
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  • Article Type: Research Article
Keyword(s): inversion; monitoring; reservoir geophysics; rock physics; time lapse

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