1887
Volume 29, Issue 3
  • ISSN: 1354-0793
  • E-ISSN:

Abstract

Time-lapse or 4D seismic data are important constraints in reservoir studies because they enable monitoring of saturation and pressure changes that result from hydrocarbon production. 4D seismic data have been quantitatively added, along with production data, in history matching or data assimilation procedures to reduce uncertainty and improve production forecasts. Before performing quantitative studies, it is important to ensure that the 4D seismic data are reliable, with minimal artefacts such as side-lobe effects that can disturb the identification of anomalies. In this work, we propose different ways of treating 4D seismic data in data assimilation for a real reservoir. Explicitly, we evaluated the impact on data assimilation results when considering different amounts of 4D information and three treatments to the identified artefacts. The treatments were: ignoring them, excluding them from data assimilation or defining no seismic changes at their locations. The results show that well and seismic matches are improved when 4D seismic data are assimilated, also improving the production predictions. Despite being a thin reservoir, assimilating two single-layer maps allowed us to predict relevant observed dynamic behaviour, such as the evolved gas trapped in the lower interval. Furthermore, when a treatment was applied to the artefacts, they produced better models than using a single two-layer map (with lower production errors and visually closer impedances to the observed data). Our recommendation is the assimilation of well and 4D seismic data, with the exclusion of unreliable information, for better life-cycle decisions.

© 2023 The Author(s). Published by The Geological Society of London for GSL and EAGE. All rights reserved

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2023-06-19
2026-04-21

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References

  1. Avansi, G.D. and Schiozer, D.J.2015. A new approach to history matching using reservoir characterization and reservoir simulation integrated studies. Paper OTC-26038-MS presented at the Offshore Technology Conference. 4–7 May 2015, Houston, Texas, USA, https://doi.org/10.4043/26038-MS
     [Google Scholar]
  2. Avansi, G.D., Maschio, C. and Schiozer, D.J.2016. Simultaneous history-matching approach by use of reservoir-characterization and reservoir-simulation studies. SPE Reservoir Evaluation & Engineering, 19, 694–712, https://doi.org/10.2118/179740-PA
     [Google Scholar]
  3. Batzle, M. and Wang, Z.1992. Seismic properties of pore fluids. Geophysics, 57, 1396–1408, https://doi.org/10.1190/1.1443207
     [Google Scholar]
  4. Buland, A. and El Ouair, Y.2006. Bayesian time-lapse inversion. Geophysics, 71, R43–R48, https://doi.org/10.1190/1.2196874
     [Google Scholar]
  5. Buland, A. and Omre, H.2003. Bayesian linearized AVO inversion. Geophysics, 68, 185–198, https://doi.org/10.1190/1.1543206
     [Google Scholar]
  6. Caldwell, J.2016. Seismic Permanent Reservoir Monitoring (PRM) – major multi-disciplinary engineering projects. In: SPG/SEG 2016 International Geophysical Conference, Beijing, China, 20–22 April 2016. Society of Exploration Geophysicists, Tulsa, OK, 501–503, https://doi.org/10.1190/IGCBeijing2016-150
     [Google Scholar]
  7. CMG2017. IMEX Black Oil & Unconventional Simulator. Computer Modelling Group Ltd (CMG), Calgary, AB, Canada.
  8. Correia, M.G., Maleki, M., Mesquita da Silva, F.B., Gomes, A.D. and Schiozer, D.J.2023. Integrated approach to improve simulation models in a deep-water heavy oil field with 4D seismic monitoring. Petroleum Geoscience, 29, https://doi.org/10.1144/petgeo2022-048
     [Google Scholar]
  9. Danaei, S., Silva Neto, G.M., Schiozer, D.J. and Davolio, A.2020. Using petro-elastic proxy model to integrate 4D seismic in ensemble based data assimilation. Journal of Petroleum Science and Engineering, 194, 107457, https://doi.org/10.1016/j.petrol.2020.107457
     [Google Scholar]
  10. Da Nóbrega, D.V., de Moraes, F.S. and Emerick, A.A.2018. Data assimilation of a legacy 4D seismic in a brown field. Journal of Geophysics and Engineering, 15, 2585–2601, https://doi.org/10.1088/1742-2140/aadd68
     [Google Scholar]
  11. Emerick, A.A.2016. Analysis of the performance of ensemble-based assimilation of production and seismic data. Journal of Petroleum Science and Engineering, 139, 219–239, https://doi.org/10.1016/j.petrol.2016.01.029
     [Google Scholar]
  12. Emerick, A.A. and Reynolds, A.C.2013a. Ensemble smoother with multiple data assimilation. Computers & Geosciences, 55, 3–15, https://doi.org/10.1016/j.cageo.2012.03.011
     [Google Scholar]
  13. Emerick, A.A. and Reynolds, A.C.2013b. History-matching production and seismic data in a real field case using the ensemble smoother with multiple data assimilation. Paper SPE-163675-MS presented at theSPE Reservoir Simulation Symposium, 18–20 February 2013, The Woodlands, Texas, USA, https://doi.org/10.2118/163675-MS
     [Google Scholar]
  14. Evensen, G.1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99, 10 143–10 162, https://doi.org/10.1029/94JC00572
     [Google Scholar]
  15. Fahimuddin, A., Aanonsen, S.I. and Skjervheim, J.A.2010. Ensemble based 4D seismic history matching: integration of different levels and types of seismic data. Paper SPE-131453-MS presented at theSPE EUROPEC/EAGE Annual Conference and Exhibition, 14–17 June 2010, Barcelona, Spain, https://doi.org/10.2118/131453-MS
     [Google Scholar]
  16. Fetkovich, M.J.1971. A simplified approach to water influx calculations-finite aquifer systems. Journal of Petroleum Technology, 23, 814–828, https://doi.org/10.2118/2603-PA
     [Google Scholar]
  17. Gassmann, F.1951. Elastic waves through a packing of spheres. Geophysics, 16, 673–685, https://doi.org/10.1190/1.1437718
     [Google Scholar]
  18. Griffiths, L., Blanchard, T.D., Edgar, J.A. and Shahraeeni, M.S.2015. Trace warping vs. impedance warping in 4D seismic inversion. In: 77th EAGE Conference and Exhibition 2015. European Association of Geoscientists & Engineers (EAGE), Houten, The Netherlands, https://doi.org/10.3997/2214-4609.201413090
     [Google Scholar]
  19. Johnston, D.H.2013. Practical Applications of Time-Lapse Seismic Data. Society of Exploration Geophysicists, Tulsa, OK.
     [Google Scholar]
  20. Kazemi, A. and Stephen, K.D.2012. Schemes for automatic history matching of reservoir modeling: a case of Nelson oilfield in UK. Petroleum Exploration and Development, 39, 349–361, https://doi.org/10.1016/S1876-3804(12)60051-2
     [Google Scholar]
  21. Lancaster, S. and Whitcombe, D.2000. Fast-track ‘coloured’ inversion. SEG Technical Program Expanded Abstracts, 2000, 3–6, https://doi.org/10.1190/1.1815711
     [Google Scholar]
  22. Leeuwenburgh, O., Meekes, S., Vandeweijer, V. and Brouwer, J.2016. Stochastic history matching to time-lapse seismic of a CO2-EOR project sector model. International Journal of Greenhouse Gas Control, 54, 441–453, https://doi.org/10.1016/j.ijggc.2016.05.027
     [Google Scholar]
  23. Liu, M. and Grana, D.2020. Time-lapse seismic history matching with an iterative ensemble smoother and deep convolutional autoencoder. Geophysics, 85, M15–M31, https://doi.org/10.1190/geo2019-0019.1
     [Google Scholar]
  24. Lopez, J.L. and Grandi, S.2019. Fully autonomous marine seismic acquisition systems for reservoir monitoring. In: Sixteenth International Congress of the Brazilian Geophysical Society & EXPOGEf, 19th August 2019–22nd August 2019, Rio de Janeiro, Brazil. Brazilian Geophysical Society, Rio de Janeiro, Brazil, http://dx.doi.org/10.22564/16cisbgf2019.337
  25. Maleki, M., Davolio, A. and Schiozer, D.J.2018. Qualitative time-lapse seismic interpretation of Norne Field to assess challenges of 4D seismic attributes. The Leading Edge, 37, 754–762, https://doi.org/10.1190/tle37100754.1
     [Google Scholar]
  26. Maleki, M., Danaei, S., Davolio, A. and José Schiozer, D.2019. Fast-track qualitative interpretation of seismic data in a permanent reservoir monitoring PRM setting for a Brazilian field. Paper SPE-196185-MS presented at theSPE Annual Technical Conference and Exhibition, 30 September–2 October 2019, Calgary, Alberta, Canada, https://doi.org/10.2118/196185-MS
     [Google Scholar]
  27. Maleki, M., Danaei, S., da Silva, F.B.M., Davolio, A. and Schiozer, D.J.2021. Stepwise uncertainty reduction in time-lapse seismic interpretation using multi-attribute analysis. Petroleum Geoscience, 27, https://doi.org/10.1144/petgeo2020-087
     [Google Scholar]
  28. Mavko, G., Mukerji, T. and Dvorkin, J.1998. The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media. 1st edn. Cambridge University Press, Cambridge, UK.
     [Google Scholar]
  29. Oliver, D.S. and Alfonzo, M.A.2018. Seismic data assimilation with an imperfect model. In: ECMOR XVI – 16th European Conference on the Mathematics of Oil Recovery. European Association of Geoscientists & Engineers (EAGE), Houten, The Netherlands, https://doi.org/10.3997/2214-4609.201802283
     [Google Scholar]
  30. Oliver, D.S., Fossum, K., Bhakta, T., Sandø, I., Nævdal, G. and Lorentzen, R.J.2021. 4D seismic history matching. Journal of Petroleum Science and Engineering, 207, 109119, https://doi.org/10.1016/j.petrol.2021.109119
     [Google Scholar]
  31. Peterson, B. and Gerhardt, A.2020. Pluto gas field: successful placement of an infill well based on 4D seismic monitoring. The Leading Edge, 39, 464–470, https://doi.org/10.1190/tle39070464.1
     [Google Scholar]
  32. Rezaei, S., Babasafari, A.A., Bashir, Y., Sambo, C., Ghosh, D. and Ahmed Salim, A.M.2020. Time lapse (4D) Seismic for Reservoir Fluid Saturation Monitoring: Application in Malaysian Basin. Petroleum & Coal, 62, 712–719.
     [Google Scholar]
  33. Robinson, E.A. and Treitel, S.2008. Digital Imaging and Deconvolution: The ABCs of Seismic Exploration and Processing. Geophysical References Series, 15. Society of Exploration Geophysicists, Tulsa, OK, 182–183.
     [Google Scholar]
  34. Rosa, D.R., Schiozer, D. and Davolio, A.2022a. Enhancing vertical resolution with 4D seismic inversion. Journal of Petroleum Science and Engineering, 212, 110291, https://doi.org/10.1016/j.petrol.2022.110291
     [Google Scholar]
  35. Rosa, D.R., Schiozer, D. and Davolio, A.2022b. Impact of model and data resolutions in 4D seismic data assimilation applied to an offshore reservoir in Brazil. Journal of Petroleum Science and Engineering, 216, 110830, https://doi.org/10.1016/j.petrol.2022.110830
     [Google Scholar]
  36. Russell, B. and Hampson, D.1991. Comparison of poststack seismic inversion methods. SEG Technical Program Expanded Abstracts, 1991, 876–878, https://doi.org/10.1190/1.1888870
     [Google Scholar]
  37. Santos, J.M.C., Davolio, A. and Schiozer, D.J.2020. Multi-attribute approach for quantifying competing time-lapse effects and implications for data assimilation. Paper SPE-201426-MS presented at theSPE Annual Technical Conference and Exhibition, 26–29 October 2020, Virtual, https://doi.org/10.2118/201426-MS
     [Google Scholar]
  38. Schiozer, D.J., dos Santos, A.A.D.S., de Graça Santos, S.M. and von Hohendorff Filho, J.C.2019. Model-based decision analysis applied to petroleum field development and management. Oil & Gas Science and Technology – Revue d'IFP Energies Nouvelles, 74, 46, https://doi.org/10.2516/ogst/2019019
     [Google Scholar]
  39. Silva Neto, G.M., Davolio, A. and Schiozer, D.J.2021. Assimilating time-lapse seismic data in the presence of significant spatially correlated model errors. Journal of Petroleum Science and Engineering, 207, 109127, https://doi.org/10.1016/j.petrol.2021.109127
     [Google Scholar]
  40. Stammeijer, J.G.F. and Hatchell, P.J.2014. Standards in 4D feasibility and interpretation. The Leading Edge, 33, 134–140, https://doi.org/10.1190/tle33020134.1
     [Google Scholar]
  41. Sun, W., Vink, J.C. and Gao, G.2017. A practical method to mitigate spurious uncertainty reduction in history matching workflows with imperfect reservoir models. Paper SPE-182599-MS presented at theSPE Reservoir Simulation Conference, 20–22 February 2017, Montgomery, Texas, USA, https://doi.org/10.2118/182599-MS
     [Google Scholar]
  42. Tian, S., MacBeth, C. and Shams, A.2012. An engineering-consistent inversion of time-lapse seismic data. In: 74th EAGE Conference and Exhibition incorporating EUROPEC 2012. European Association of Geoscientists & Engineers (EAGE), Houten, The Netherlands, https://doi.org/10.3997/2214-4609.20148836
     [Google Scholar]
  43. van Leeuwen, P.J. and Evensen, G.1996. Data assimilation and inverse methods in terms of a probabilistic formulation. Monthly Weather Review, 124, 2898–2913, https://doi.org/10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2
     [Google Scholar]
  44. Wong, L.J., Amini, H. and MacBeth, C.2020. Noisy 4D seismic data interpretation: case study of a Brazilian carbonate reservoir. The Leading Edge, 39, 488–496, https://doi.org/10.1190/tle39070488.1
     [Google Scholar]
  45. Yin, Z., Feng, T. and MacBeth, C.2019. Fast assimilation of frequently acquired 4D seismic data for reservoir history matching. Computers & Geosciences, 128, 30–40, https://doi.org/10.1016/j.cageo.2019.04.001
     [Google Scholar]
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Evaluating the impact of 4D seismic data artefacts in data assimilation
Petroleum Geoscience 29, petgeo2022-069 (2023); https://doi.org/10.1144/petgeo2022-069
/content/journals/10.1144/petgeo2022-069
/content/journals/10.1144/petgeo2022-069
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