1887
Volume 67 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The ultimate goal of reservoir simulation in reservoir surveillance technology is to estimate long‐term production forecasting and to plan development and management of petroleum fields. However, maintaining reliable reservoir models which honour available static and dynamic data, involve inherent risks due to the uncertainties in space and time of the distribution of hydrocarbons inside reservoirs. Recent applications have shown that these uncertainties can be reduced by quantitative integration of seismic data into the reservoir modelling workflows to identify which areas and reservoir attributes of the model should be updated. This work aims using seismic data to reduce ambiguity in calibrating reservoir flow simulation model with an uncertain petro‐elastic model, proposing a circular workflow of inverted seismic impedance (3D and 4D) and engineering studies, with emphasis on the interface between static and dynamic models. The main contribution is to develop an updating procedure for adjusting reservoir simulation response before using it in the production forecasting and enhance the interpretive capability of reservoir properties. Accordingly, the workflow evaluates consistency of reservoir simulation model and inverted seismic impedance, assisted by production history data, to close the loop between reservoir engineering and seismic domains. The methodology is evaluated in a complex, faulted, sandstone reservoir, the Norne benchmark field, where a significant reservoir behaviour understanding (about the static and dynamic reservoir properties) is obtained towards the quantitative integration of seismic impedance data. This leads to diagnosis of the reservoir flow simulation reliability and generation of an updated simulation model consistent with observed seismic and well production history data, as well as a calibrated petro‐elastic model. Furthermore, as Norne Field is a benchmark case, this study can be considered to enrich the discussions over deterministic or probabilistic history matching studies.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12717
2018-12-03
2024-04-26
Loading full text...

Full text loading...

References

  1. AminiH.2014. A pragmatic approach to simulator‐to‐seismic modelling for 4D seismic interpretation . PhD thesis, Heriot‐Watt University.
  2. AschjemG.2013. Mapping reservoir changes using 4D seismic on the Norne G‐segment, Norwegian Sea . MSc dissertation, Norwegian University of Science and Technology, Norway.
  3. AvansiG.D., MaschioC. and SchiozerD.J.2016. Simultaneous history‐matching approach by use of reservoir‐characterization and reservoir‐simulation studies. SPE Reservoir Evaluation & Engineering19, 694–712.
    [Google Scholar]
  4. AyzenbergM., HustoftL., SkjeiN. and FengT.2013. Seismic 4D inversion for quantitative use in automated history matching. 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013, London.
  5. BoutteD.2007. Through the continuous life cycle of reservoir, geophysics makes its mark. The Leading Edge26, 1376–1379.
    [Google Scholar]
  6. BricenoA., MacBethC. and MangriotisM.D.2016. Towards an effective petroelastic model for simulator to seismic studies. 78th Conference & Exhibition, EAGE, Vienna.
  7. ByerleyG., SingerL. and RoseP.2016. Resaturated pay: a new infill target type identified through the application and continuous improvement of 4D seismic at the Forties Field. The Leading Edge35, 831–838.
    [Google Scholar]
  8. ChengN. and OsdalB.2008. Updating the Norne reservoir model using 4D seismic data. IO Workshop, Center for Integrated Operations in the Petroleum Industry at NTNU, Trondheim, Norway.
  9. CorreiaG.G.2017. Integration of reservoir characterization with history matching guided by pilot wells: application to the Norne field . PhD thesis, University of Campinas (UNICAMP), Brazil.
  10. CorzoM., MacBethC. and BarkvedO.2013. Estimation of pore‐pressure change in a compacting reservoir from time‐lapse seismic data. Geophysical Prospecting61, 1022–1034.
    [Google Scholar]
  11. DadashpourM., CiaurriD.E., KleppeJ. and LandrøM.2009. Porosity and permeability estimation by integration of production and time‐lapse near and far offset seismic data. Journal of Geophysics and Engineering6, 325–344.
    [Google Scholar]
  12. DadashpourM., CiaurriD.E., MukerjiT., KleppeJ. and LandrøM., 2010. A derivative‐free approach for the estimation of porosity and permeability using time‐lapse seismic and production data. Journal of Geophysics and Engineering7, 351–638.
    [Google Scholar]
  13. DallandA., WorsleyD. and OfstadK.1988. A lithostratigraphic scheme for the Mesozoic and cenozoic succession offshore mid‐ and northern Norway. Norwegian Petroleum Directorate Bulletin4, 1–65.
    [Google Scholar]
  14. De MarsilyG., LavedanG., BoucherM. and FasaninoG.1984. Interpretation of interference tests in a well field using geostatistical techniques to fit the permeability distribution in a reservoir model. In: Geostatistics Natural Resources Characterization, Part 2 (eds G.Verly , M.David , A.G.Journel and A.Maréchal ), pp. 831–849. Reidel, Dordrecht, Holland.
    [Google Scholar]
  15. El OuairY., LygrenM., OsdalB., HusbyO. and SpringerM.2005. Integrated reservoir management approach: from time‐lapse acquisition to reservoir model update at the Norne field. International Petroleum Technology Conference, Doha, Qatar, 21–23.
  16. FanchiJ.R.2001. Principles of Applied Reservoir Simulation, 2nd edn. Gulf Professional Publishing, Houston, TX.
    [Google Scholar]
  17. GassmannF.1951. Elastic waves through a packing of spheres. Geophysics16, 673–685.
    [Google Scholar]
  18. GosselinO., AanonsenS.I., AavatsmarkI., CominelliA., GonardR., KolasinskiM., et al. 2003. History matching using time‐lapse seismic (HUTS). SPE Annual Technical Conference and Exhibition, SPE 84464, Dubai.
  19. GuderianK., KleemeyerM., KjeldstaadA., PetterssonS.E. and RehlingJ.2003. Draugen field: successful reservoir management using 4D seismic. 65th Conference & Exhibition, EAGE.
  20. HammerE., MørkM.B.E. and NæssA.2010. Facies controls on the distribution of diagenesis and compaction in fluvial‐deltaic deposits. Marine and Petroleum Geology27, 1737–1751.
    [Google Scholar]
  21. HoffmanB.T.2005. Geologically consistent history matching while perturbing facies. PhD thesis, Stanford University.
  22. HoffmanB.T. and CaersJ.2007. History matching by jointly perturbing local facies proportions and their spatial distribution: application to a Norne sea reservoir. Journal of Petroleum Science and Engineering57, 257–272.
    [Google Scholar]
  23. HuangY., AlsosT., SørensenH.M. and TianS.2013. Proving the value of 4D seismic data in the late‐life field – case study of the Norne main field. First Break31, 57–67.
    [Google Scholar]
  24. HuangY., MacBethC., BarkvedO., GestelJ.P. and DybvikO.P.2011. Enhanced dynamic interpretation from correlating well activity to frequently acquired 4D seismic signatures. The Leading Edge30, 1042–1050.
    [Google Scholar]
  25. JohnstonD.H.2013. Practical Applications of Time‐Lapse Seismic Data. Society of Exploration Geophysicists, Tulsa, OK.
    [Google Scholar]
  26. LandrøM.2001. Discrimination between pressure and fluid saturation changes from time‐lapse seismic data. Geophysics66, 836–844.
    [Google Scholar]
  27. LandrøM., SolheimO.A., HidleE., EkrenB.O. and StrønenL.K.1999. The Gullfaks 4D seismic study. Petroleum Geoscience5, 213–226.
    [Google Scholar]
  28. LuciaF.J.2007. Carbonate Reservoir Characterization. An Integrated Approach. Springer, Austin, TX.
    [Google Scholar]
  29. LygrenM., HusbyO., OsdalB., El OuairY. and SpringerM.2005. History matching using 4D seismic and pressure data on the Norne field. 67th Conference & Exhibition, EAGE, Madrid.
  30. MacBethC.2004. A classification for the pressure‐sensitivity properties of a sandstone rock frame. Geophysics69, 497–510.
    [Google Scholar]
  31. MalekiM., DavolioA. and SchiozerD.J.2016. Reservoir characterization using model based post‐stack inversion: a case study in Norne field to show the impact of the number of wells in inversion. Rio Oil & Gas Expo and Conference, IPB1957_16.
  32. MalekiM., DavolioA. and SchiozerD.J.2017. Qualitative time‐lapse seimic interpretation; seismic amplitude or impedance? A case study in Norne benchmark case. 2017 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics).
  33. MalekiM., DavolioA. and SchiozerD.J.2018a. Using simulation and production data to resolve ambiguity in interpreting 4D seismic inverted impedance in the Norne field. Petroleum Geoscience24, 335–347.
    [Google Scholar]
  34. MalekiM., DavolioA. and SchiozerD.J.2018b. Qualitative time‐lapse seismic interpretation of the Norne field to assess the challenges of 4D seismic attributes. The Leading Edge37, 754–762.
    [Google Scholar]
  35. OsdalB.2004. Using high quality and repeatable Q‐marine data in reservoir monitoring on the Norne field. 66th Conference and Exhibition, EAGE, Paris.
  36. OsdalB. and AlsosT.2002. Seismic modelling of eclipse simulations and comparison with real 4D data at the Norne field. 64th Conference & Exhibition, EAGE, Florence.
  37. OsdalB. and AlsosT.2010. Norne 4D and reservoir management – The keys to success. 72nd EAGE Conference & Exhibition incorporating SPE EUROPEC 2010.
  38. OsdalB., HusbyO., AronsenH.A., ChenN. and AlsosT.2006. Mapping the fluid front and pressure buildup using 4D data on Norne Field. The Leading Edge25, 1134–1141.
    [Google Scholar]
  39. PetersL., ArtsR., BrouwerG., GeelC., CullickS., LorentzenR.J., et al. 2010. Results of the brugge benchmark study for flooding optimization and history matching. SPE Reservoir Evaluation & Engineering13, 391–405.
    [Google Scholar]
  40. RoggeroF., DingD.Y., BerthetP., LeratO., CapJ. and SchreiberP.E.2007. Matching of production history and 4D seismic data: application to the Girassol field, offshore Angola. SPE Annual Technical Conference & Exhibition, SPE‐109929‐MS, Anaheim, CA.
  41. RoggeroF., LeratO., DingD.Y., BerthetP., BordenaveC., LefeuvreF., et al. 2012. History matching of production and 4D seismic data: application to the Girassol field, offshore Angola. Oil & Gas Science and Technology67, 237–262.
    [Google Scholar]
  42. RwechunguraR.W., SuwartadiE., DadashpourM., KleppeJ. and FossB.2010. The Norne field case – a unique comparative case study. SPE Intelligent Energy Conference and Exhibition, SP‐127538, Utrecht.
  43. SantosJ.M.C.2017. Semi‐quantitative 4D seismic interpretation integrated with reservoir simulation: application to the Norne field. MSc dissertation, University of Campinas (UNICAMP), Brazil.
  44. SantosJ.M.C., DavolioA. and SchiozerD.J.2016. 4D seismic interpretation of the Norne field – a semi‐quantitative approach. 78th Conference & Exhibition, EAGE, Vienna.
  45. SkjervheimJ.A., EvensenG., AanonsenS.I., RuudB.O. and JohansenT.A. 2007. Incorporating 4D seismic data in reservoir simulation models using Ensemble Kalman filter. SPE Journal, 12, 282–292.
    [Google Scholar]
  46. SteffensenI. and KarstadtP.I.1996. Norne field development – fast track from discovery to production. Journal of Petroleum Technology48, 296–339.
    [Google Scholar]
  47. StephenK.D. and MacBethC.2008. Reducing reservoir prediction uncertainty by updating a stochastic model using seismic history matching. SPE Reservoir Evaluation & Engineering11, 991–999.
    [Google Scholar]
  48. StephenK.D., SoldoJ., MacBethC. and ChristieM.A.2006. Multiple model seismic and production history matching: a case study. SPE Journal11, 418–430.
    [Google Scholar]
  49. StronenK.L. and DigranesP.2000. The Gullfaks field – 4D seismic enhances oil recovery and improves the reservoir description. 62th Conference & Exhibition, EAGE, Glasgow.
  50. SumanA.2013. Joint inversion of production and time‐lapse seismic data: application to Norne field. PhD thesis, Stanford University.
  51. SwiecickiT., GibbsP., FarrowG. and CowardM.P.1998. A tectonostratigraphic framework for the mid‐Norway region. Marine and Petroleum Geology15, 245–276.
    [Google Scholar]
  52. TianS., MacbethC. and ShamsA.2014. Updating the reservoir model using engineering‐consistent 4D seismic inversion. 76th Conference & Exhibition, EAGE, Amsterdam.
  53. TuraA., BarkerT., CattermoleP., CollinsC., DavisJ., HatchellP., et al. 2005. Monitoring primary depletion reservoirs using amplitudes and time shifts from high‐repeat seismic surveys. The Leading Edge24, 1214–1221.
    [Google Scholar]
  54. VerloS.B. and HerlandM.2008. Development of a field case with real production and 4D data from the Norne Field as a benchmark case for future reservoir simulation model testing. MSc dissertation, Norwegian University of Science and Technology, Norway.
  55. YanT.2014. History matching of production and 4D seismic data: Application to the Norne Field. MSc dissertation, Norwegian University of Science and Technology, Norway.
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12717
Loading
/content/journals/10.1111/1365-2478.12717
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): History matching; Monitoring; Reservoir simulation; Time lapse

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error