1887
Volume 23, Issue 2
  • ISSN: 1354-0793
  • E-ISSN:

Abstract

Hydrocarbon reservoirs often contain thin and irregularly shaped low-permeability heterogeneities at the interface between sedimentary bodies that can act as barriers or baffles where they are aligned perpendicular to the flow. These heterogeneities are often not explicitly captured in simulation models because they occur over lengthscales smaller than a single simulation grid block and, as such, are ubiquitously captured in flow simulations using transmissibility multipliers. This parameterization implies that the properties of the fine-scale heterogeneities are a direct function of the properties in the adjacent grid cells. A new methodology is proposed where fine-scale heterogeneities are modelled using properties attached to surfaces and updated independently from other uncertain reservoir properties present during assisted history matching. This method is validated via a synthetic reservoir simulation model, using the ensemble Kalman filter to assimilate production history. The update of geometrical and petrophysical parameters related to fine-scale heterogeneity improves the history match and production forecast in comparison to traditional implicit techniques that do not honour the characteristics of the heterogeneity. The results indicate that, for history matching, it can be important to capture fine-scale heterogeneities independently from other uncertain reservoir properties even if the fine-scale heterogeneities do not exert a large control on reservoir response.

Loading

Article metrics loading...

/content/journals/10.1144/petgeo2016-045
2016-10-14
2020-06-02
Loading full text...

Full text loading...

References

  1. Aavatsmark, I.
    2002. An introduction to multipoint flux approximations for quadrilateral grids. Computational Geosciences, 6, 405–432, http://doi.org/10.1023/A:1021291114475
    [Google Scholar]
  2. Abreu, V., Sullivan, M., Pirmez, C. & Mohrig, D.
    2003. Lateral accretion packages (LAPs): an important reservoir element in deep water sinuous channels. Marine and Petroleum Geology, 20, 631–648, http://doi.org/10.1016/j.marpetgeo.2003.08.003
    [Google Scholar]
  3. Alpak, F.O. & van der Vlugt, F.F.
    2014. Shale-drape modeling for the geologically consistent simulation of clastic reservoirs. SPE Journal, 19, 832–844, http://doi.org/10.2118/169820-PA
    [Google Scholar]
  4. Alpak, F.O., Barton, M.D. & Naruk, S.J.
    2013. The impact of fine-scale turbidite channel architecture on deep-water reservoir performance. American Association of Petroleum Geologists Bulletin, 97, 251–284, http://doi.org/10.1306/04021211067
    [Google Scholar]
  5. Anderson, J.L.
    2009. Ensemble kalman filters for large geophysical applications. IEE Control Systems Magazine, 29, 66–82.
    [Google Scholar]
  6. Astrakova, A. & Oliver, D.S.
    2014. Conditioning truncated pluri-Gaussian models to facies observations in ensemble-Kalman-based data assimilation. Mathematical Geosciences, 12, 382–391.
    [Google Scholar]
  7. Barton, M., O'Byrne, C., Pirmez, C., Prather, B., Van der Vlugt, F., Alpak, F.O. & Sylvester, Z.
    2010. Turbidite channel architecture: recognizing and quantifying the distribution of channel-base drapes using core and dipmeter data. In: Poppelreiter, M., Garcia-Carballido, C. & Kraaijveld, M. (eds) Dipmeter and Borehole Image Log Technology. American Association of Petroleum Geologists, Memoirs, 92, 195–210.
    [Google Scholar]
  8. Begg, S.H. & King, P.R.
    1985. Modelling the effects of shales on reservoir performance: Calculation of effective vertical permeability. Paper 13529 presented at theSPE Reservoir Simulation Symposium, 10–13 February 1985, Dallas, Texas, USA, http://doi.org/10.2118/13529-MS
    [Google Scholar]
  9. Burton, D. & Wood, L.J.
    2011. Quantitative shale characterization of the tidally influenced Sego Sandstone. American Association of Petroleum Geologists Bulletin, 95, 1207–1226, http://doi.org/10.1306/12081010119
    [Google Scholar]
  10. Chen, Y. & Oliver, D.S.
    2010. Cross-covariances and localization for EnKF in multiphase flow data assimilation. Computational Geosciences, 14, 579–601.
    [Google Scholar]
  11. Eide, C.H., Howell, J. & Buckley, S.
    2014. Distribution of discontinuous mudstone beds within wave-dominated shallow-marine deposits: Star Point Sandstone and Blackhawk Formation, Eastern Utah. American Association of Petroleum Geologists Bulletin, 98, 1401–1429, http://doi.org/10.1306/01201413106
    [Google Scholar]
  12. Enge, H.D., Howell, J.A. & Buckley, S.J.
    2010. The geometry and internal architecture of stream mouth bars in the Panther Tongue and the Ferron Sandstone members, Utah, USA. Journal of Sedimentary Research, 80, 1018–1031, http://doi.org/10.2110/jsr.2010.088
    [Google Scholar]
  13. Evensen, G.
    1994. Sequential data assimilation with a nonlinear quasigeostrophic model using monte carlo methods to do forecast error statistics. Journal of Geophysical Research, 99, 10,143–10,162.
    [Google Scholar]
  14. 2009. Data Assimilation: The Ensemble Kalman Filter. Springer Science & Business Media, Dordrecht, The Netherlands.
    [Google Scholar]
  15. Evensen, G., Hove, J., Meisingset, H.C., Reiso, E., Seim, K.S. & Espelid,S.O.
    2007. Using the EnKF for assisted history matching of a North Sea reservoir. Paper SPE-106184 presented at theSPE Reservoir Simulation Symposium, 26–28 February 2007, Houston, Texas, USA.
    [Google Scholar]
  16. Graham, G.H., Jackson, M.D. & Hampson, G.J.
    2015. Three-dimensional modeling of clinoforms in shallow-marine reservoirs: Part 2. Impact on fluid flow and hydrocarbon recovery in fluvial-dominated deltaic reservoirs. American Association of Petroleum Geologists Bulletin, 99, 1049–1080, http://doi.org/10.1306/01191513191
    [Google Scholar]
  17. Gringarten, E.J., Mallet, J.-L., Alapetite, J. & Leflon, B.
    2005. Stochastic modeling of fluvial reservoir: The YACS approach. Paper SPE 97271 presented at the SPE Annual Technical Conference and Exhibition, 9–12 October 2005, Dallas, Texas, USA, http://doi.org/10.2118/97271-MS
    [Google Scholar]
  18. Haldorsen, H.H. & Lake, L.W.
    1984. A new approach to shale management in field-scale models. Society of Petroleum Engineers Journal, 24, 447–457, http://doi.org/10.2118/10976-PA
    [Google Scholar]
  19. Hassanpour, M.M., Pyrcz, M.J. & Deutsch, C.V.
    2013. Improved geostatistical models of inclined heterolithic strata for McMurray Formation, Alberta, Canada. American Association of Petroleum Geologists Bulletin, 97, 1209–1224.
    [Google Scholar]
  20. Heinemann, Z.E., Brand, C.W., Munka, M. & Chen, Y.M.
    1991. Modeling reservoir geometry with irregular grids. SPE Reservoir Engineering, 6, 225–232, http://doi.org/10.2118/18412-PA
    [Google Scholar]
  21. Holgate, N.E., Hampson, G.J., Jackson, C.A.-L. & Petersen, S.A.
    2014. Constraining uncertainty in interpretation of seismically imaged clinoforms in deltaic reservoirs, Troll field, Norwegian North Sea: Insights from forward seismic models of outcrop analogs. American Association of Petroleum Geologists Bulletin, 98, 2629–2663, http://doi.org/10.1306/05281413152
    [Google Scholar]
  22. Howell, J., Vassel, Å. & Aune, T.
    2008. Modelling of dipping clinoform barriers within deltaic outcrop analogues from the Cretaceous Western Interior Basin, USA. In: Robinson, A., Griffiths, P., Price, S., Hegre, J. & Muggeridge, A. (eds) The Future of Geological Modelling in Hydrocarbon Development. Geological Society, London, Special Publications, 309, 99–121, http://doi.org/10.1144/SP309.8
    [Google Scholar]
  23. Jackson, M.D. & Muggeridge, A.H.
    2000. Effect of discontinuous shales on reservoir performance during horizontal waterflooding. SPE Journal, 5, 446–455.
    [Google Scholar]
  24. Jackson, M.D., Hampson, G.J. & Sech, R.P.
    2009. Three-dimensional modeling of a shoreface–shelf parasequence reservoir analog: Part 2. Geologic controls on fluid flow and hydrocarbon recovery. American Association of Petroleum Geologists Bulletin, 93, 1183–1208, http://doi.org/10.1306/05110908145
    [Google Scholar]
  25. Jackson, M.D., Hampson, G.J., Saunders, J.H., El-Sheikh, A., Graham, G.H. & Massart, B.Y.G.
    2013. Surface-based reservoir modelling for flow simulation. In: Martinius, A.W., Howell, J.A. & Good, T.R. (eds) Sediment-Body Geometry and Heterogeneity: Analogue Studies for Modelling the Subsurface. Geological Society, London, Special Publications, 387, 270–292, http://doi.org/10.1144/SP387.2
    [Google Scholar]
  26. Jackson, M., Percival, J. et al.
    2015. Reservoir modeling for flow simulation by use of surfaces, adaptive unstructured meshes, and an overlapping-control-volume finite-element method. SPE Reservoir Evaluation & Engineering, 18, 115–132, http://doi.org/10.2118/163633-PA
    [Google Scholar]
  27. Jafarpour, B. & Khodabakhshi, M.
    2011. A probability conditioning method (PCM) for non-linear flow data integration into multipoint statistical facies simulation. Mathematical Geosciences, 43, 133–164.
    [Google Scholar]
  28. Kalman, R.E.
    1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82, 35–45, http://doi.org/10.1115/1.3662552
    [Google Scholar]
  29. Kolla, V., Bourges, P., Urruty, J.-M. & Safa, P.
    2001. Evolution of deep-water Tertiary sinuous channels offshore Angola (West Africa) and implications for reservoir architecture. American Association of Petroleum Geologists Bulletin, 85, 1373–1405.
    [Google Scholar]
  30. Labourdette, R.
    2008. ‘LOSCS’ Lateral Offset Stacked Channel Simulations: Towards geometrical modelling of turbidite elementary channels. Basin Research, 20, 431–444, http://doi.org/10.1111/j.1365-2117.2008.00361.x
    [Google Scholar]
  31. Labourdette, R. & Bez, M.
    2010. Element migration in turbidite systems: Random or systematic depositional processes?American Association of Petroleum Geologists Bulletin, 94, 345–368, http://doi.org/10.1306/09010909035
    [Google Scholar]
  32. Labourdette, R., Poncet, J., Seguin, J., Temple, F., Hegre, J. & Irving, A.
    2006. Three-dimensional modelling of stacked turbidite channels in West Africa: impact on dynamic reservoir simulations. Petroleum Geoscience, 12, 335–345, http://doi.org/10.1144/1354-079306-705
    [Google Scholar]
  33. Le Dimet, F.-X. & Talagrand, O.
    1986. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus A, 38A, 97–110, http://doi.org/10.1111/j.1600-0870.1986.tb00459.x
    [Google Scholar]
  34. Lee, S.H., Wolfsteiner, C., Durlofsky, L.J., Jenny, P. & Tchelepi, H.A.
    2003. New developments in multiblock reservoir simulation: black oil modeling, nonmatching subdomains and near-well upscaling. Paper SPE 79682 presented at theSPE Reservoir Simulation Symposium, 3–5 February 2003, Houston, Texas, USA, http://doi.org/10.2118/79682-MS
    [Google Scholar]
  35. Li, H. & Caers, J.
    2007. Hierarchical modeling and history matching of multiscale flow barriers in channelized reservoir. Paper SPE 109252 presented at theSPE Annual Technical Conference and Exhibition, 11–14 November 2007, Anaheim, California, USA, http://doi.org/10.2118/109252-MS
    [Google Scholar]
  36. Lorentzen, R.J., Fjelde, K.K., Frøyen, J., Lage, A.V.M. & Naevdal, G.
    2001. Underbalanced and low-head drilling operations: Real time data interpretation and decision support. Paper SPE 71384 presented at theSPE Annual Technical Conference and Exhibition, 30 September–3 October 2001, New Orleans, Louisiana, USA.
    [Google Scholar]
  37. Manzocchi, T., Walsh, J.J., Nell, P. & Yielding, G.
    1999. Fault transmissibility multipliers for flow simulation models. Petroleum Geoscience, 5, 53–63, http://doi.org/10.1144/petgeo.5.1.53
    [Google Scholar]
  38. Mayall, M., Jones, E. & Casey, M.
    2006. Turbidite channel reservoirs – Key elements in facies prediction and effective development. Marine and Petroleum Geology, 23, 821–841, http://doi.org/10.1016/j.marpetgeo.2006.08.001
    [Google Scholar]
  39. Moore, I.
    2014. Alba Field – how seismic technologies have influenced reservoir characterization and field development. In: McKie, T., Rose, P.T.S., Hartley, A.J., Jones, D.W. & Armstrong, T.L. (eds) Tertiary Deep-Marine Reservoirs of the North Sea Region. Geological Society, London, Special Publications, 403, 355–379, http://doi.org/10.1144/SP403.6
    [Google Scholar]
  40. Onyeagoro, K., Naruk, S., Van der Vlugt, F., Barton, M., Pirmez, C. & O'Byrne, C.
    2007. Impact of structural and stratigraphic heterogeneities in deep water development. Paper SPE 111909 presented at theNigeria Annual International Conference and Exhibition, 6–8 August 2007, Abuja, Nigeria, http://doi.org/10.2118/111909-MS
    [Google Scholar]
  41. Prélat, A., Hodgson, D.M. & Flint, S.S.
    2009. Evolution, architecture and hierarchy of distributary deep-water deposits: a high-resolution outcrop investigation from the Permian Karoo Basin, South Africa. Sedimentology, 56, 2132–2154, http://doi.org/10.1111/j.1365-3091.2009.01073.x
    [Google Scholar]
  42. Pyrcz, M.J., Catuneanu, O. & Deutsch, C.V.
    2005. Stochastic surface-based modeling of turbidite lobes. American Association of Petroleum Geologists Bulletin, 89, 177–191, http://doi.org/10.1306/09220403112
    [Google Scholar]
  43. Rose, P.T.S. & Pyle, J.R.
    2014. The habitat of bypassed pay in the Forties Field. In: McKie, T., Rose, P.T.S., Hartley, A.J., Jones, D.W. & Armstrong, T.L. (eds) Tertiary Deep-Marine Reservoirs of the North Sea Region. Geological Society, London, Special Publications, 403, 333–354, http://doi.org/10.1144/SP403.3
    [Google Scholar]
  44. Schlumberger
    . 2010. ECLIPSE Reservoir Simulation Software. ECLIPSE 100 Technical Description. Schlumberger, Houston, TX, USA.
    [Google Scholar]
  45. Sebacher, B., Stordal, A.S. & Hanea, R.
    2015. Bridging multipoint statistics and truncated gaussian fields for improved estimation of channelized reservoir with ensemble methods. Computational Geosciences, 47, 345–367.
    [Google Scholar]
  46. Sech, R.P., Jackson, M.D. & Hampson, G.J.
    2009. Three-dimensional modeling of a shoreface–shelf parasequence reservoir analog: Part 1. Surface-based modeling to capture high-resolution facies architecture. American Association of Petroleum Geologists Bulletin, 93, 1155–1181, http://doi.org/10.1306/05110908144
    [Google Scholar]
  47. Seiler, A., Aanonsen, S.I., Evensen, G. & Rivenws, J.C.
    2010. Structural surface uncertainty modeling and updating using the ensemble Kalman filter. SPE Journal, 15, 1062–1076, http://doi.org/10.2118/125352-PA
    [Google Scholar]
  48. Sprague, A.R.G., Sullivan, M.D. et al.
    2002. The physical stratigraphy of deep-water strata: A hierarchical approach to the analysis of genetically related stratigraphic elements for improved reservoir prediction. AAPG Search and Discovery Article 90007 presented at theAAPG Annual Meeting, March 10–13, 2002, Houston, Texas, USA, http://www.searchanddiscovery.com/abstracts/pdf/2002/annual/CONTENTS.HTML
    [Google Scholar]
  49. Stright, L.
    2006. Modeling, upscaling, and history matching thin, irregularly-shaped flow barriers: a comprehensive approach for predicting reservoir connectivity. Paper SPE 106528 presented at theSPE Annual Technical Conference and Exhibition, 24–27 September 2006, San Antonio, Texas, USA, http://doi.org/10.2118/106528-STU
    [Google Scholar]
  50. Tavassoli, Z., Carter, J.N. & King, P.R.
    2004. Errors in history matching. SPE Journal, 9, 352–361, http://doi.org/10.2118/86883-PA
    [Google Scholar]
  51. Wen, R.
    2005. 3d geologic modelling of channellized reservoirs: applications in seismic attribute facies classification. First Break, 23, (12), 71–78.
    [Google Scholar]
  52. White, C.D. & Willis, B.J.
    2000. A method to estimate length distributions from outcrop data. Mathematical Geology, 32, 389–419, http://doi.org/10.1023/A:1007510615051
    [Google Scholar]
  53. White, C.D., Novakovic, D., Dutton, S.P. & Willis, B.J.
    2003. A geostatistical model for calcite concretions in sandstone. Mathematical Geology, 35, 549–575, http://doi.org/10.1023/A:1026282602013
    [Google Scholar]
  54. Willis, B.J. & Tang, H.
    2010. Three-dimensional connectivity of point-bar deposits. Journal of Sedimentary Research, 80, 440–454, http://doi.org/10.2110/jsr.2010.046
    [Google Scholar]
  55. Wu, X.-H. & Parashkevov, R.
    2009. Effect of grid deviation on flow solutions. SPE Journal, 14, 67–77, http://doi.org/10.2118/92868-PA
    [Google Scholar]
  56. Zafari, M. & Reynolds, A.C.
    2007. Assessing the uncertainty in reservoir description and performance predictions with the ensemble kalman filter. SPE Journal, 12, 382–391.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1144/petgeo2016-045
Loading
/content/journals/10.1144/petgeo2016-045
Loading

Data & Media loading...

  • Article Type: Research Article
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