Object-Based Modeling with Dense Well Data

  • Ragnar Hauge
  • Maria Vigsnes
  • Bjørn Fjellvoll
  • Markus Lund Vevle
  • Arne Skorstad
Chapter
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 19)

Abstract

Although object models are popular with geologists due to their ability to control the geometries that are produced, they tend to have convergence issues when conditioning on complex well patterns. In this paper, we present a new well conditioning algorithm that utilizes more local data when generating channels. We show that this algorithm performs better than the currently commercially available state-of-the-art object model and thus makes object models viable in modern mature field well settings.

Bibliography

  1. Boisvert J, Pyrcz M (2014) Conditioning 3D object based models to a large number of wells: a channel example. Mathematics of planet earth: proceedings of the 15th annual conference of IAMG. Springer, p 575–579Google Scholar
  2. Bridge J, Leeder M (1979) A simulation model of alluvial stratigraphy. Sedimentology 26:617–644CrossRefGoogle Scholar
  3. Deutsch CV, Wang L (1996) Hierarchical object-based stochastic modeling of fluvial reservoirs. Math Geol 28:857–880CrossRefGoogle Scholar
  4. Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biomterika 57:97–109CrossRefGoogle Scholar
  5. Hauge R, Holden L, Syversveen A (2007) Well conditioning in object models. Math Geol 39:383–398CrossRefGoogle Scholar
  6. Henrion V, Caumon G, Cherpeau N (2010) ODSIM: an object-distance simulation method for conditioning complex natural structures. Math Geosci 42:911–924CrossRefGoogle Scholar
  7. Holden L, Hauge R, Skare Ø, Skorstad A (1998) Modeling of fluvial reservoirs with object models. Math Geol 30:473–496CrossRefGoogle Scholar
  8. Skorstad A, Hauge R, Holden L (1999) Well conditioning in a fluvial reservoir model. Math Geol 31:857–872CrossRefGoogle Scholar
  9. Strebelle S (2002) Conditional simulation of complex geological structures using multiple-point statistics. Math Geol 34:1–21CrossRefGoogle Scholar
  10. Vargas-Guzman J, Al-Quassab H (2006) Spatial conditional simulation for facies objects for modelling complex clastic reservoirs. J Petrol Sci Eng 54:1–9CrossRefGoogle Scholar
  11. Viseur S, Shtuka A, Mallet J (1998) New fast, stochastic, boolean simulation of fluvial deposits. Proceedings, 1998 SPE ATCE, New Orleans, USA. SPE 49281Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ragnar Hauge
    • 1
  • Maria Vigsnes
    • 1
  • Bjørn Fjellvoll
    • 1
  • Markus Lund Vevle
    • 2
  • Arne Skorstad
    • 2
  1. 1.Norwegian Computing CenterOsloNorway
  2. 2.Roxar Software Solutions ASLysakerNorway

Personalised recommendations