1887
  1. Home
  2. A-Z Publications
  3. Petroleum Geoscience
  4. Volume 27, Issue 3
  5. Article
Volume 27, Issue 3
  • ISSN: 1354-0793
  • E-ISSN:

Abstract

The construction of subsurface reservoir models is typically aided by the use of outcrops and modern analogue systems. We show how process-based models of depositional systems help to develop and substantiate reservoir architectural concepts. Process-based models can simulate assumptions relating to the physical processes influencing sedimentary deposition, accumulation and erosion on the resultant 3D sediment distribution. In this manner, a complete suite of analogue geometries can be produced by implementing different sets of boundary conditions based on hypotheses of depositional controls. Simulations are therefore not driven by a desired/defined outcome in the depositional patterns, but their application to date in reservoir modelling workflows has been limited because they cannot be conditioned to data such as well logs or seismic information.

In this study a reservoir modelling methodology is presented that addresses this problem using a two-step approach: process-based models producing 3D sediment distributions that are subsequently used to generate training images for multi-point geostatistics.

The approach has been tested on a dataset derived from a well-exposed outcrop from central Utah. The Ferron Sandstone Member includes a shallow-marine deltaic interval that has been digitally mapped using a high-resolution unmanned aerial vehicle (UAV) survey in 3D to produce a virtual outcrop (VO). The VO was used as the basis to build a semi-deterministic outcrop reference model (ORM) against which to compare the results of the combined process/multiple-point statistics (MPS) geostatistical realizations. Models were compared statically and dynamically through flow simulation.

When used with a dense well dataset, the MPS realizations struggle to account for the high levels of non-stationarity inherent in the depositional system that are captured in the process-based training image. When trends are extracted from the outcrop analogue and used to condition the simulation, the geologically realistic geometries and spatial relationships from the process-based models are directly imparted onto the modelling domain, whilst simultaneously allowing the facies models to be conditioned to subsurface data.

When sense-checked against preserved analogues, this approach reproduces more realistic architectures than traditional, more stochastic techniques.

© 2021 The Author(s). Published by The Geological Society of London for GSL and EAGE. All rights reserved
Loading

Article metrics loading...

/content/journals/10.1144/petgeo2020-012
2021-05-20
2024-04-25

  • From This Site

    /content/journals/10.1144/petgeo2020-012
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aarnes, A., van der Vegt, H., Hauge, R., Fjellvol, B. and Nordahl, K.
    2019. Utilizing sedimentary process-based models as training images for multipoint facies simulation. Bulletin of Canadian Petroleum Geology, 63, 217–230, https://doi.org/10.35767/gscpgbull.67.4.217
     [Google Scholar]
  2. Aas, T., Howell, J., Janocko, M. and Midtkandal, I.
    2010. Re-created early Oligocene seabed bathymetry and process-based simulations of the Peïra Cava turbidite system. Journal of the Geological Society, London, 167, 857–875, https://doi.org/10.1144/0016-76492009-005
     [Google Scholar]
  3. Aas, T., Basani, R., Howell, J. and Hansen, E.
    2014. Forward modelling as a method for predicting the distribution of deep-marine sands: an example from the Peïra Cava Sub-basin. Geological Society, London, Special Publications , 387, 247–269, https://doi.org/10.1144/SP387.9
     [Google Scholar]
  4. Ainsworth, R., Vakarelov, B. and Nanson, R.
    2011. Dynamic spatial and temporal prediction of changes in depositional processes on clastic shorelines: toward improved subsurface uncertainty reduction and management. AAPG Bulletin, 95, 267–297, https://doi.org/10.1306/06301010036
     [Google Scholar]
  5. Anderson, P.B. and Ryer, A.
    2004. Regional stratigraphy of the Ferron Sandstone. AAPG Studies in Geology , 50, 125–199.
     [Google Scholar]
  6. Anderson, P.B., Chidsey, T., Jr, Ryer, T., Mattson, A. and Adams, R.
    1996. Geologic framework of the Ivie Creek case study area. In: Chidsey, T., Jr (ed.) Geological and Petrophysical Characterization of the Ferron Sandstone for 3D Simulation of A Fluvial–Deltaic Reservoir. Utah Geological Survey, Salt Lake City, UT, 274–317.
     [Google Scholar]
  7. Anderson, P.B., Chidsey, T.J. and Ryer, T.A.
    1997. Fluvial–deltaic sedimentation and stratigraphy of the Ferron Sandstone. Brigham Young University Geology Studies , 42, 135–154.
     [Google Scholar]
  8. Anderson, P.B., Chidsey, T., McClure, K., Mattson, A. and Snelgrove, S.
    2004. Ferron Sandstone Stratigraphic Cross Sections, Ivie Creek Area, Emery County, Utah. Utah Geological Survey Open-File Report, 390, 1–17.
     [Google Scholar]
  9. Barton, M.
    1995. Sequence Stratigraphy, Facies Architecture, and Permeability Structure of Fluvial-Deltaic Reservoir Analogs: Cretaceous Ferron Sandstone. Central Utah (Ferron GRI Fieldtrip Guidebook). Bureau of Economic Geology, The University of Texas at Austin, Austin, TX.
     [Google Scholar]
  10. Barton, M. and Tyler, N.
    1991. Quantification of permeability structure in distributary-channel deposits, Ferron Sandstone, Utah. Utah Geological Association Publication , 19, 273–282.
     [Google Scholar]
  11. Barton, M.D., Angle, E. and Tyler, T.
    2004. Stratigraphic architecture of fluvial-deltaic sandstones from the Ferron Sandstone outcrop, east-central Utah. AAPG Studies in Geology , 50, 451–498.
     [Google Scholar]
  12. Bayer, P., Comunian, A., Höyng, D. and Mariethoz, G.
    2015. High resolution multi-facies realizations of sedimentary reservoir and aquifer analogs. Scientific Data, 2, 293–308, https://doi.org/10.1038/sdata.2015.33
     [Google Scholar]
  13. Behrens, R., MacLeod, M., Tran, T. and Alimi, A.
    1998. Incorporating seismic attribute maps in 3d reservoir models. Society of Petroleum Engineers Journal, 1, 112–126.
     [Google Scholar]
  14. Bentley, M. and Smith, S.
    2008. Scenario-based reservoir modelling: the need for more determinism and less anchoring. Geological Society, London, Special Publications , 309, 145–159, https://doi.org/10.1144/SP309.11
     [Google Scholar]
  15. Bertoncello, A., Sun, T., Li, H., Mariethoz, G. and Caers, J.
    2013. Conditioning surface-based geological models to well and thickness data. Mathematical Geosciences, 45, 873–893, https://doi.org/10.1007/s11004-013-9455-4
     [Google Scholar]
  16. Bhattacharya, J.P.
    2006. Deltas. SEPM Special Publications , 84, 237–292.
     [Google Scholar]
  17. Bhattacharya, J.P. and Davies, R.
    2001. Growth faults at the prodelta to delta-front transition, Cretaceous Ferron Sandstone, Utah. Marine and Petroleum Geology, 18, 525–534, https://doi.org/10.1016/S0264-8172(01)00015-0
     [Google Scholar]
  18. Boucher, A.
    2009. Considering complex training images with search tree partitioning. Computers and Geosciences, 35, 1151–1158, https://doi.org/10.1016/j.cageo.2008.03.011
     [Google Scholar]
  19. 2011. Strategies for modeling with multiple-point simulation algorithms. Paper presented at Closing the Gap: 2011 Gussow Geoscience Conference, 3–5 October 2011, Banff, Alberta, Canada.
     [Google Scholar]
  20. Bozhenyuk, N., Belkina, V. and Strekalov, A.
    2018. Creating a geological model of the field taking into account information on horizontal wells and the analysis of inter-well reservoir connectivity. IOP Conference Series: Earth and Environmental Science , 181, 01200, https://doi.org/10.1088/1755-1315/181/1/012004
     [Google Scholar]
  21. Braathen, A., Midtkandal, I., Mulrooney, M., Appleyard, T., Haile, B. and van Yperen, A.
    2018. Growth-faults from delta collapse – structural and sedimentological investigation of the Last Chance delta, Ferron Sandstone, Utah. Basin Research, 30, 688–707, https://doi.org/10.1111/bre.12271
     [Google Scholar]
  22. Burgess, P., Lammers, H., Van Oosterhout, C. and Granjeon, D.
    2006. Multivariate sequence stratigraphy: Tackling complexity and uncertainty with stratigraphic forward modeling, multiple scenarios, and conditional frequency maps. AAPG Bulletin, 90, 1883, https://doi.org/10.1306/06260605081
     [Google Scholar]
  23. Caers, J. and Zhang, T.
    2004. Multiple-point geostatistics: A quantitative vehicle for integrating geologic analogs into multiple reservoir models. AAPG Memoirs , 80, 383–394.
     [Google Scholar]
  24. Caers, J., Strebelle, S. and Payrazyan, P.
    2003. Stochastic integration of seismic data and geologic scenarios: A West African submarine channel saga. The Leading Edge, 22, 192–196, https://doi.org/10.1190/1.1564521
     [Google Scholar]
  25. Cannon, S.
    2018. Reservoir Modelling: A Practical Guide. Wiley-Blackwell, Hoboken, NJ.
     [Google Scholar]
  26. Charvin, K., Gallagher, K., Hampson, G. and Labourdette, R.
    2009a. A Bayesian approach to inverse modelling of stratigraphy, part 1: method. Basin Research, 21, 5–25, https://doi.org/10.1111/j.1365-2117.2008.00369.x
     [Google Scholar]
  27. Charvin, K., Hampson, G., Gallagher, K. and Labourdete, R.
    2009b. A Bayesain approach to inverse modelling of stratigraphy, part 2: validation tests. Basin Research, 21, 27–45, https://doi.org/10.1111/j.1365-2117.2008.00370.x
     [Google Scholar]
  28. Chidsey, T.C., Jr, Adams, R.D. and Morris, T.H.
    2004. Regional to Wellbore Analog for Fluvial-Deltaic Reservoir Modeling: The Ferron Sandstone of Utah. AAPG Studies in Geology, 50.
     [Google Scholar]
  29. Colombera, L., Felletti, F., Mountney, N. and McCaffery, W.
    2012. A database approach for constraining stochastic simulations of the sedimentary heterogeneity of fluvial reservoirs. AAPG Bulletin, 96, 2143–2166, https://doi.org/10.1306/04211211179
     [Google Scholar]
  30. Colombera, L., Mountney, N. and McCaffrey, W.
    2014. Models for guiding and ranking well-to-well correlations of channel bodies in fluvial reservoirs. AAPG Bulletin, 98, 1943–1965, https://doi.org/10.1306/05061413153
     [Google Scholar]
  31. Comunian, A., Renard, P., Straubhaar, J. and Bayer, B.
    2011. Three- dimensional high resolution fluvio-glacial aquifer analog – part 2: Geostatistical modeling. Journal of Hydrology, 405, 10–23, https://doi.org/10.1016/j.jhydrol.2011.03.037
     [Google Scholar]
  32. Cotter, E.
    1976. The role of deltas in the evolution of the Ferron Sandstone and its coals. Brigham Young University Sudies in Geology , 22, 15–41.
     [Google Scholar]
  33. Deutsch, C.V. and Journel, A.G.
    1992. GSLIB: Geostatistical Software Library and User's Guide. Oxford University Press, New York.
     [Google Scholar]
  34. Deutsch, C.V., Srinivasan, S. and Mo, Y.
    1996. Geostatistical reservoir modeling accounting for precision and scale of seismic data. Paper SPE-36497 presented at theSPE Annual Technical Conference and Exhibition, 6–9 October 1996, Denver, Colorado, USA, https://doi.org/10.2118/36497-MS
     [Google Scholar]
  35. Deveugle, P., Jackson, M. et al.
    2011. Characterization of stratigraphic architecture and its impact on fluid flow in a fluvial-dominated reservoir analogue: Upper Cretaceous Ferron Sandstone Member, Utah. AAPG Bulletin, 95, 693–727, https://doi.org/10.1306/09271010025
     [Google Scholar]
  36. 2014. A comparative study of reservoir modeling techniques and their impact on predicted performance of fluvial-dominated deltaic reservoirscomparison of reservoir modeling techniques. AAPG Bulletin, 98, 729–763, https://doi.org/10.1306/08281313035
     [Google Scholar]
  37. de Vries, L., Carrera, J., Falivene, O., Gratacós, O. and Slooten, L.
    2008. Application of multiple point geostatistics to non-stationary images. Mathematical Geosciences, 41, 29–42, https://doi.org/10.1007/s11004-008-9188-y
     [Google Scholar]
  38. Dreyer, T., Fält, L., Høy, T., Knarud, R., Steel, R. and Cuevas, J.
    1993. Sedimentary Architecture of Field Analogues for Reservoir Information (SAFARI): A case study of the fluvial Escanilla formation, Spanish Pyrenees. In: Flint, S. and Bryant, I. (eds) The Geological Modelling of Hydrocarbon Reservoirs and Outcrop Analogues. Blackwell, Oxford, UK, 57–80, https://doi.org/10.1002/9781444303957.ch3
     [Google Scholar]
  39. Eidsvik, J., Avseth, P., Omre, H., Mukerji, T. and Mavko, G.
    2004. Stochastic reservoir characterization using prestack seismic data. Geophyics, 69, 978–993, https://doi.org/10.1190/1.1778241
     [Google Scholar]
  40. Enge, H. and Howell, J.
    2010. Impact of deltaic clinothems on reservoir performance: dynamic studies of reservoir analogs from the Ferron Sandstone Member and Panther Tongue, Utah. AAPG Bulletin, 94, 139–161, https://doi.org/10.1306/07060908112
     [Google Scholar]
  41. Enge, H., Buckley, S., Rotevatn, A. and Howell, J.
    2007. From outcrop to reservoir simulation model: Workflow and procedures. Geosphere, 3, 469, https://doi.org/10.1130/GES00099.1
     [Google Scholar]
  42. Falivene, O., Arbués, P., Howell, J., Munoz, J., Fernández, O., Muñoz, M. and Cabrera, A.
    2006. A FORTRAN program to introduce field-measured sedimentary logs into reservoir modelling packages. Computers and Geosciences, 32, 1519–1522, https://doi.org/10.1016/j.cageo.2006.01.005
     [Google Scholar]
  43. Forster, C. and Snelgrove, S.
    2002. Ferron Sandstone Permeability Database, Ivie Creek Area, Emery County, Utah. Utah Geological Survey Open-File Report, 389.
     [Google Scholar]
  44. Gardner, M.
    1995. Tectonic and eustatic controls on the stratal architecture of mid-Cretaceous stratigraphic sequences, central Western Interior foreland Basin of North America. Society for Economic Paleontologists and Mineralogists Special Publications , 52, 243–281.
     [Google Scholar]
  45. Garrison, J. and Van den Bergh, T.
    2004. High-resolution depositional sequence stratigraphy of the Upper Ferron Sandstone Last Chance Delta: An application of coal-zone stratigraphy. AAPG Studies in Geology , 50, 125–199.
     [Google Scholar]
  46. Gawith, D. and Gutteridge, P.
    1996. Seismic validation of reservoir simulation using a shared earth model. Petroleum Geoscience, 2, 97–103, https://doi.org/10.1144/petgeo.2.2.97
     [Google Scholar]
  47. Gilbert, R., Liu, Y., Abriel, W. and Preece, R.
    2004. Reservoir modeling: Integrating various data at appropriate scales. The Leading Edge, 23, 784–788, https://doi.org/10.1190/1.1786903
     [Google Scholar]
  48. Graham, G., Jackson, M. and Hampson, G.
    2015. Three-dimensional modeling of clinoforms in shallow-marine reservoirs: Part 1. Concepts and application. AAPG Bulletin, 99, 1013–1047, https://doi.org/10.1306/01191513190
     [Google Scholar]
  49. Guardiano, F. and Srivastava, R.
    1993. Multivariate geostatistics: Beyond bivariate moments. In: Soares, A. (ed.) Geostatistics Tróia ’92. Quantitative Geology and Geostatistics, 5. Springer, Dordrecht, The Netherlands, 133–144, https://doi.org/10.1007/978-94-011-1739-5_12
     [Google Scholar]
  50. Hale, L.
    1972. Depositional history of the Ferron Sandstone, central Utah. Utah Geological Association Publication , 2, 29–40.
     [Google Scholar]
  51. Hampson, G., Morris, J. and Johnson, H.
    2015. Synthesis of time-stratigraphic relationships and their impact on hydrocarbon reservoir distribution and performance, Bridport Sand Formation, Wessex Basin, UK. Geological Society, London, Special Publications , 404, 199–222, https://doi.org/10.1144/SP404.2
     [Google Scholar]
  52. Howell, J.A., Vassel, Å. and Aune, T.
    2008. Modelling of dipping clinoform barriers within deltaic outcrop analogues from the Cretaceous Western Interior Basin, USA. Geological Society, London, Special Publications , 309, 91–121, https://doi.org/10.1144/SP309.8
     [Google Scholar]
  53. Howell, J.A., Martinius, A.W. and Good, T.R.
    2014. The application of outcrop analogues in geological modelling: a review, present status and future outlook. Geological Society, London, Special Publications , 387, 1–25, https://doi.org/10.1144/SP387.12
     [Google Scholar]
  54. Hu, L.Y. and Chugunova, T.
    2008. Multiple-point geostatistics for modeling subsurface heterogeneity: A comprehensive review. Water Resources Research, 44, 133–146, https://doi.org/10.1029/2008WR006993
     [Google Scholar]
  55. Hu, L.Y., Liu, Y., Scheepens, C., Shultz, A.W. and Thompson, R.D.
    2014. Multiple-point simulation with an existing reservoir model as training image. Mathematical Geosciences, 46, 227–240, https://doi.org/10.1007/s11004-013-9488-8
     [Google Scholar]
  56. Jackson, M., Hampson, G. and Sech, R.
    2009. Three-dimensional modeling of a shoreface-shelf parasequence reservoir analog: Part 2. Geologic controls on fluid flow and hydrocarbon recovery. AAPG Bulletin, 93, 1183–1208, https://doi.org/10.1306/05110908145
     [Google Scholar]
  57. Journel, A.G.
    2002. Combining knowledge from diverse sources: an alternative to traditional data independence hypotheses. Mathematical Geology, 34, 573–596, https://doi.org/10.1023/A:1016047012594
     [Google Scholar]
  58. Kaplan, R., Pyrcz, M. and Strebelle, S.
    2017. Deepwater Reservoir Connectivity Reproduction From MPS and Process-Mimicking Geostatistical Methods. Springer International, New York, 601–611.
     [Google Scholar]
  59. Kaufman, E.
    1977. Geological and biological overview: Western Interior Cretaceous Basin. The Mountain Geologist, 95, 75–99.
     [Google Scholar]
  60. Labourdette, R., Herge, J., Imbert, P. and Insalaco, E.
    2008. Reservoir-scale 3D sedimentary modelling: approaches to integrate sedimentology into a reservoir characterization workflow. Geological Society, London, Special Publications , 309, 75–85, https://doi.org/10.1144/SP309.6
     [Google Scholar]
  61. Laigle, L., Joseph, P., de Marsily, G. and Violette, S.
    2010. 3-D process modelling of ancient storm-dominated deposits by an event-based approach. Island Sustainability, 130, 171–182, https://doi.org/10.1016/j.margeo.2012.11.007
     [Google Scholar]
  62. Larue, D. and Hodavik, J.
    2006. Connectivity of channelized reservoirs: a modelling approach. Petroleum Geoscience, 12, 291–308, https://doi.org/10.1144/1354-079306-699
     [Google Scholar]
  63. Lesser, G., Roelvink, J., van Kester, J. and Stelling, G.
    2004. Development and validation of a three-dimensional morphological model. Coastal Engineering, 51, 883–915, https://doi.org/10.1016/j.coastaleng.2004.07.014
     [Google Scholar]
  64. Leuangthong, O. and Deutsch, C.V.
    2004. Transformation of residuals to avoid artifacts in geostatistical modelling with a trend. Mathematical Geology, 36, 287–305, https://doi.org/10.1023/B:MATG.0000028438.48852.b0
     [Google Scholar]
  65. Liu, S., Nummedal, D. and Gurnis, M.
    2014. Dynamic versus flexural controls of Late Cretaceous Western Interior Basin, USA. Earth and Planetary Science Letters, 389, 221–229, https://doi.org/10.1016/j.epsl.2014.01.006
     [Google Scholar]
  66. Lupton, C.
    1914. Oil and gas near Green River, Grand County, Utah. United States Geological Survey Bulletin , 541, 115–133.
     [Google Scholar]
  67. Manzocchi, T., Carter, J.N. et al.
    2008. Sensitivity of the impact of geological uncertainty on production from faulted and unfaulted shallow-marine oil reservoirs: objectives and methods. Petroleum Geoscience, 14, 3–15, https://doi.org/10.1144/1354-079307-790
     [Google Scholar]
  68. Mariethoz, G. and Caers, J.
    2015. Multiple-Point Geostatistics: Stochastic Modeling with Training Images. Wiley Blackwell, Chichester, UK.
     [Google Scholar]
  69. Mariethoz, G., Renard, P. and Froidevaux, R.
    2009. Integrating collocated auxiliary parameters in geostatistical simulations using joint probability distributions and probability aggregation. Water Resources Research, 45, W08421, https://doi.org/10.1029/2008WR007408
     [Google Scholar]
  70. Mariethoz, G., Renard, P. and Straubhaar, J.
    2010. The Direct Sampling method to perform multiple-point geostatistical simulations. Water Resources Research, 46, W11536, 10.1029/2008WR007621
     [Google Scholar]
  71. Matheron, G., Beucher, H., Galli, A., Guérillot, D. and Ravenne, C.
    1987. Conditional simulation of the geometry of fluvio-deltaic reservoirs. Paper SPE-16753 presented at theSPE Annual Technical Conference and Exhibition, 27–30 September 1987, Dallas, Texas, USA, https://doi.org/10.2118/16753-MS
     [Google Scholar]
  72. Michael, H., Li, H., Boucher, A., Sun, T., Caers, J. and Gorelick, S.
    2010. Combining geologic-process models and geostatistics for conditional simulation of 3-D subsurface heterogeneity. Water Resources Research, 46, W05527, https://doi.org/10.1029/2009WR008414
     [Google Scholar]
  73. Miller, J., Sun, T., Li, H., Stewart, J., Genty, C., Li, D. and Lyttle, C.
    2008. Direct modeling of reservoirs through forward process-based models: Can we get there?Paper IPTC-12729 presented at theInternational Petroleum Technology Conference, 3–5 December 2008, Kuala Lumpur, Malaysia, https://doi.org/10.2523/IPTC-12729-MS
     [Google Scholar]
  74. Olariu, C. and Bhattacharya, J.P.
    2006. Terminal distributary channels and delta front architecture of river-dominated delta systems. Journal of Sedimentary Research, 76, 212–233, https://doi.org/10.2110/jsr.2006.026
     [Google Scholar]
  75. Olneva, T., Kuzmin, D., Rasskazova, S. and Timirgalin, A.
    2018. Big data approach for geological study of the Big region, West Siberia. Paper SPE-191726 presented at theSPE Annual Technical Conference and Exhibition, 24–26 September 2018, Dallas, Texas, USA, https://doi.org/10.2118/191726-MS
     [Google Scholar]
  76. Owen, A., Nichols, G., Hartley, A., Weissmann, G. and Scuderi, L.
    2015. Quantification of a distributive fluvial system: The Salt Wash DFS of the Morrison Formation, SW U.S.A. Journal of Sedimentary Research, 85, 544–561, https://doi.org/10.2110/jsr.2015.35
     [Google Scholar]
  77. Pickel, A., Frechette, J., Comunian, A. and Weissmann, G.
    2015. Building a training image with digital outcrop models. Journal of Hydrology, 531, 53–61, https://doi.org/10.1016/j.jhydrol.2015.08.049
     [Google Scholar]
  78. Prélat, A., Hodgson, D. and Flint, 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, https://doi.org/10.1111/j.1365-3091.2009.01073.x
     [Google Scholar]
  79. Prélat, A., Covault, J., Hodgson, D., Fildani, A. and Flint, S.
    2010. Intrinsic controls on the range of volumes, morphologies, and dimensions of submarine lobes. Sedimentary Geology, 232, 66–76, https://doi.org/10.1016/j.sedgeo.2010.09.010
     [Google Scholar]
  80. Pyrcz, M. and Deutsch, C.
    2005. Conditioning Event-Based Fluvial Models. Springer, Dordrecht, The Netherlands, 135–144.
     [Google Scholar]
  81. Pyrcz, M. and Strebelle, S.
    2006. Event-based geostatistical modeling of deep-water systems. Paper presented at theTwenty-Sixth Annual Research Conference: Reservoir Characterization: Integrating Technology and Business Practices, 3–6 December 2006, Houston, Texas, USA.
     [Google Scholar]
  82. Pyrcz, M., Catuneanu, O. and Deutsch, C.
    2005. Stochastic surface-based modeling of turbidite lobes. AAPG Bulletin, 89, 177–191, https://doi.org/10.1306/09220403112
     [Google Scholar]
  83. Pyrcz, M., McHargue, T., Clark, J., Sullivan, M. and Strebelle, S.
    2012. Event-based geostatistical modeling: Description and applications. In: Abrahamsen, P., Hauge, R. and Kolbjørnsen, O. (eds) Geostatistics Oslo 2012. Quantitative Geology and Geostatistics, 17. Springer, Dordrecht, The Netherlands, 27–38, https://doi.org/10.1007/978-94-007-4153-9_3
     [Google Scholar]
  84. Pyrcz, M., Sech, R., Covault, J., Willis, B., Sylvester, Z. and Sun, T.
    2015. Stratigraphic rule-based reservoir modeling. Bulletin of Canadian Petroleum Geology, 63, 287–303, https://doi.org/10.2113/gscpgbull.63.4.287
     [Google Scholar]
  85. Renard, P. and Allard, D.
    2013. Connectivity metrics for subsurface flow and transport. Advances in Water Resources, 51, 168–196, https://doi.org/10.1016/j.advwatres.2011.12.001
     [Google Scholar]
  86. Ringrose, P. and Bentley, M.
    2015. Reservoir Model Design: A Practitioner's Guide. Springer, Dordrecht, The Netherlands.
     [Google Scholar]
  87. Rittersbacher, A., Buckley, S., Howell, J., Hampson, G. and Vallet, J.
    2013. Helicopter-based laser scanning: a method for quantitative analysis of large-scale sedimentary architecture. Geological Society, London, Special Publications , 387, 185–202, https://doi.org/10.1144/SP387.3
     [Google Scholar]
  88. Ryer, T.
    1981. Deltaic coals of Ferron Sandstone Member of Mancos Shale: Predictive model for Cretaceous coal-bearing strata of the Western Interior. AAPG Bulletin, 65, 2323–2340.
     [Google Scholar]
  89. Ryer, T.A.
    1991. Stratigraphy, facies, and depositional history of the Ferron Sandstone of Muddy Creek, east-central Utah. Utah Geological Association Publication , 19, 45–54.
     [Google Scholar]
  90. Ryer, T.
    1993. The autochthonous component of cyclicity in shoreline deposits of the Upper Cretaceous Ferron Sandstone, Central Utah. AAPG Bulletin, 77, 175.
     [Google Scholar]
  91. Ryer, T. and McPhillips, M.
    1983. Early Late Cretaceous paleogeography of East central Utah. In: Reynolds, M. and Dolly, E. (eds) Mesozoic Paleogeography of the West-Central United States: Rocky Mountain Paleogeography Symposium 2. Society of Economic Paleontologists and Mineralogists, Denver, CO,253–272.
     [Google Scholar]
  92. Ryer, T. and Thomas, A.
    2004. Previous studies of the Ferron Sandstone. AAPG Studies in Geology , 50, 3–38.
     [Google Scholar]
  93. Salleh, Z. and Poh, K.
    2017. Integration of geology and geophysical data in reservoir characterisation – a case study of the K Field. Search and Discovery Article #20387, AAPG/SEG International Conference and Exhibition, 3–6 April 2016, Barcelona, Spain.
     [Google Scholar]
  94. Sánden, A., Boerboom, H., Donselaar, M. and Storms, J.
    2016. Process-based modelling of sediment distribution in fluvial crevasse splays. Paper presented at the78th EAGE Conference and Exhibition 2016, 30 May–2 June 2016, Vienna, Austria.
     [Google Scholar]
  95. Saussus, D. and Sams, M.
    2012. Facies as the key to using seismic inversion for modelling reservoir properties. First Break, 30, 45–52, https://doi.org/10.3997/1365-2397.2012009
     [Google Scholar]
  96. Sech, R., Jackson, M. and Hampson, G.
    2009. Three-dimensional modeling of a shoreface–shelf parasequence reservoir analog: Part 1. Surface-based modeling to capture high-resolution facies architecture. AAPG Bulletin, 93, 1155–1181, https://doi.org/10.1306/05110908144
     [Google Scholar]
  97. Spetsakis, M. and Aloimonos, Y.
    1991. A multiframe approach to visual motion perception. International Journal of Computer Vision, 6, 245–255, https://doi.org/10.1007/BF00115698
     [Google Scholar]
  98. Spychala, Y., Hodgson, D. and Lee, D.
    2017. Autogenic controls on hybrid bed distribution in submarine lobe complexes. Marine and Petroleum Geology, 88, 1078–1093, https://doi.org/10.1016/j.marpetgeo.2017.09.005
     [Google Scholar]
  99. Strebelle, S.
    2002. Conditional simulation of complex geological structures using multiple-point statistics. Mathematical Geology, 34, 1–21, https://doi.org/10.1023/A:1014009426274
     [Google Scholar]
  100. 2012. Multiple-point geostatistics: from theory to practice. Paper presented at theNinth International Geostatistics Congress, 11–15 June 2012, Oslo, Norway.
     [Google Scholar]
  101. and Levy, M. 2008. Using multiple-point statistics to build geologically realistic reservoir models: the MPS/FDM workflow. Geological Society, London, Special Publications , 309, 67–74, https://doi.org/10.1144/SP309.5
     [Google Scholar]
  102. and Zhang, T. 2004. Non-stationary multiple-point geostatistical models. In: Leuangthong, O. and Deutsch, C. (eds) Geostatistics Banff. Springer, Dordrecht, The Netherlands, 235–244.
     [Google Scholar]
  103. Strebelle, S., Payrazyan, K. and Caers, J.
    2003. Modeling of a deepwater turbidite reservoir conditional to seismic data using principal component analysis and multiple-point geostatistics. Society of Petroleum Engineers Journal, 8, 227–235, https://doi.org/10.2118/85962-PA
     [Google Scholar]
  104. Szeliski, R. and Kang, S.
    1994. Recovering 3-D shape and motion from image streams using nonlinear least squares. Journal of Visual Communication and Image Representation, 5, 10–28, https://doi.org/10.1006/jvci.1994.1002
     [Google Scholar]
  105. van der Vegt, H., Storms, J., Walstra, D-J., Nordahl, K., Howes, N. and Martinius, A.
    2020. Grain size fractionation by process-driven sorting in sandy muddy deltas. The Depositional Record, 6, 217–235, https://doi.org/10.1002/dep2.85
     [Google Scholar]
  106. Wang, J. and Dou, Q.
    2010. Integration of 3D seismic attributes into stochastic reservoir models using iterative vertical resolution modeling methodology. Paper SPE-132654 presented at theSPE Western Regional Meeting, 27–29 May 2010, Anaheim, California, USA, https://doi.org/10.2118/132654-MS
     [Google Scholar]
  107. Weinbrandt, R., Trentham, R. and Robinson, W.
    1998. Incorporating seismic attribute porosity into a flow model of the Grayburg Reservoir. Paper SPE-39666 presented at theSPE/DOE Improved Oil Recovery Symposium, 19–22 April 1998, Tulsa, Oklahoma, USA, https://doi.org/10.2118/39666-MS
     [Google Scholar]
  108. Wellner, R., Beaubouef, R., Van Wagoner, J., Roberts, H. and Sun, T.
    2005. Jet-plume depositional bodies – the primary building blocks of Wax Lake Delta. Gulf Coast Association of Geological Societies Transactions, 55, 867–909.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1144/petgeo2020-012
Loading
Combining process-based models and multiple-point geostatistics for improved reservoir modelling
Petroleum Geoscience 27, petgeo2020-012 (2021); https://doi.org/10.1144/petgeo2020-012
/content/journals/10.1144/petgeo2020-012
/content/journals/10.1144/petgeo2020-012
Loading

Data & Media loading...

  • Article Type: Research Article

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