Moscow University Geology Bulletin

, Volume 67, Issue 1, pp 43–51 | Cite as

The influence of conceptual model of sedimentary formation hydraulic heterogeneity on contaminant transport simulation

  • S. P. PozdniakovEmail author
  • V. A. Bakshevskaya
  • I. V. Krohicheva
  • V. V. Danilov
  • A. A. Zubkov


Development of heterogeneity model of layered sandy-clay formation and impact of this model on transport is considered. The lithological data of more than 250 wells that captured 300 meters formation at the investigated area of 40 km2 are used for model of heterogeneity construction. Two models of heterogeneity were developed with using these well data: TP/MC model based on 3D Markov chain simulation for four hydrofacies and 2D kriging interpolation of thicknesses of elementary lithological layers. Simulation of conservative transport by particle tracking algorithm shows that horizontal transport along layers is similar for both models. The main difference is in vertical transport cross formation bedding. The kriging interpolation model gives more conservative results than TP/MC model due to larger characteristic horizontal length of layers in the kriging model. As the result vertical effective hydraulic conductivity of formation is in two times larger and the first particle arriving time is in four times faster in TP/MC model.


heterogeneity Markov chains kriging anisotropy effective parameters contaminant transport 


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  1. Bishop, C.E., Wallace, J., and Lowe, M., Recommended Septic Tank Soil-Absorption-System Densities for Cache Valley, Cache County, Utah, Report of Investigation. Utah Geological Survey. Division of Utah Department of Natural Resources, 2007, vol. 257.Google Scholar
  2. Bulynnikova, A.A. and Surkov, V.S., Geologicheskoe stroenie i perspektivy neftegazonosnosti yugo-vostochnoi chasti Zapadno-Sibirskoi nizmennosti (Geological Structure and Oil-and-Gas Bearing Prospects of the Southeastern Part of West Siberia), Moscow: Gostoptekhizdat, 1962.Google Scholar
  3. Carle, S.F. and Fogg, G.E., Transition Probability-Based Indicator Geostatistics, Math. Geol., 1996, vol. 28, no. 4, pp. 453–477.CrossRefGoogle Scholar
  4. Carle, S.F. and Fogg, G.E., Moceling Spatial Variability with One- and Multi-Dimensional Markov Chains, Math. Geol., 1997, vol. 29, no. 7, pp. 891–918.CrossRefGoogle Scholar
  5. Carle, S.F., T-PROGS: Transition Probability Geostatistical Software, Univ. of California, 1998.Google Scholar
  6. Chernyaev, E.V., Koshkarev, V.L., Kolmakova, O.V., et al., Geologic-Geophysical Model of the Seversk Area, Izv. Tomsk. Politekh. Univ., 2002, vol. 305, no. 6, pp. 414–433.Google Scholar
  7. Chiang, W.H. and Kinzelbach, W., 3D-Groundwater Modeling with PMWI, Berlin: Springer, 2001.Google Scholar
  8. Dai, Z., Wolfsberg, A., Lu, Z., and Ritzi, R.J., Representing Aquifer Architecture in Macrodispersivity Models with An Analytical Solution of the Transition Probability Matrix, Geophys. Res. Lett., 2007, vol. 34, no. 6, p. L20406.CrossRefGoogle Scholar
  9. Dubrule, O., Geostatistics for Seismic Data Integration in Earth Models, Zeist: EAGE, 2002.Google Scholar
  10. Elfeki, A. and Dekking, M., A Markov Chain Model for Subsurface Characterization: Theory and Applications, Math. Geol., 2001, vol. 33, no. 5, pp. 569–589.CrossRefGoogle Scholar
  11. Engdahl, N.B., Vogler, E.T., and Weissmann, G.S., Evaluation of Aquifer Heterogeneity Effects on River Flow Loss Using a Transition Probability Framework, Water Resour. Res., 2010, vol. 46, p. 13.Google Scholar
  12. Falivene, O., Cabrera, L., Munoz, J.A., et al., Statistical Grid-Based Facies Reconstruction and Modelling for Sedimentary Bodies. Alluvial-Palustrine and Turbiditic Examples, Geologica Acta, 2007, vol. 5, no. 3, pp. 199–230.Google Scholar
  13. Gol’bert, A.V., Osnovy regional’noi paleoklimatologii (Fundamentals of Regional Paleoclimatology), Moscow: Nedra, 1987.Google Scholar
  14. Koltermann, C.E. and Gorelick, S.M., Heterogeneity in Sedimentary Deposits. A Review of Structure-Imitating, Process-Imitating, and Descriptive Approaches, Water Resour. Res., 1996, vol. 32, no. 9, pp. 2617–2658.CrossRefGoogle Scholar
  15. Pinus, O.V. and Pairazyan, K.V., The Peculiarities of Geological Modeling of Productive Beds of Fluvial Origin, Geol. Nefti Gaza, 2008, no. 1, pp. 25–30.Google Scholar
  16. Podobina, V.M., Foraminifery, biostratigrafiya verkhnego mela i paleogena Zapadnoi Sibiri (Foraminifera and Biostratigraphy of Upper Cretaceous and Paleogene in West Siberia), Tomsk: Tomskii gosudarstvennyi univ., 2009.Google Scholar
  17. Pozdniakov, S.P. and Tsang, S.F., A Semianalytical Approach To Spatial Averaging of Hydraulic Conductivity in Heterogeneous Aquifers, J. Hydrology, 1996, vol. 216, nos. 1–2, pp. 78–98.Google Scholar
  18. Pozdniakov, S.P., Bakshevskaya, V.A., Zubkov, A.A., Danilov, V.V., Rybalchenko, A.I., and Tsang, C.-F., Modeling of Waste Injection in Heterogeneous Sandy Clay Formation, in Underground Injection Science and Technology, Amsterdam: Elsevier, 2005, pp. 203–218.CrossRefGoogle Scholar
  19. Proce, C.J., Ritzi, R.W., Dominic, D.F., and Dai, Z., Modeling Multiscale Heterogeneity and Aquifer Interconnectivity, Ground Water, 2004, vol. 42, no. 5, pp. 658–670.CrossRefGoogle Scholar
  20. Ritzi, R.W., Dominic, D.F., Slesers, A.J., et al., Comparing Statistical Models of Physical Heterogeneity in Buried-Valley Aquifers, Water. Res. Res, 2000, vol. 35, no. 11, pp. 3179–3192.CrossRefGoogle Scholar
  21. Rybal’chenko, A.I., Pimenov, M.K., Kostin, P.P., et al., Glubinnoe zakhoronenie zhidkikh i radioaktivnykh otkhodov (Deep Burial of Liquid and Radioactive Waste), Moscow: IzdAT, 1994.Google Scholar
  22. Sivakumar, B., Harter, T., and Zhang, H., A Fractal Investigation of Solute Travel Time in a Heterogeneous Aquifer: Transition Probability: Markov Chain Representation, Ecol. Modell., 2005, vol. 182, nos. 3–4, pp. 355–370.CrossRefGoogle Scholar
  23. Sun, A.Y., Ritzi, R.W., and Sims, D.W., Characterization and Modeling of Spatial Variability in a Complex Alluvial Aquifer: Implications on Solute Transport, Water. Resour. Res., 2008, vol. 44, p. 16.Google Scholar
  24. Weissmann, G.S. and Fogg, G.E., Multi-Scale Alluvial Fan Heterogeneity Modeled with Transition Probability Geostatistics in a Sequence Stratigraphic Framework, J. Hydrology, 1999, vol. 226, pp. 48–65.CrossRefGoogle Scholar
  25. Weissmann, G.S., Carle, S.F., and Fogg, G.E., Three-Dimensional Hydrofacies Modeling Based on Soil Surveys and Transition Probability Geostatistics, Water Resour. Res., 1999, vol. 35, no. 6, pp. 1761–1770.CrossRefGoogle Scholar
  26. Yong, Z. and Fogg, G.E., Simulation of Multi-Scale Heterogeneity of Porous Media and Parameter Sensitivity Analysis, Sci. China, Ser. E: Technol. Sci., 2003, vol. 46, no. 5, pp. 459–476.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2012

Authors and Affiliations

  • S. P. Pozdniakov
    • 1
    Email author
  • V. A. Bakshevskaya
    • 2
  • I. V. Krohicheva
    • 3
  • V. V. Danilov
    • 4
  • A. A. Zubkov
    • 4
  1. 1.Department of Hydrogeology, Faculty of GeologyMoscow State UniversityMoscowRussia
  2. 2.Institute of Water ProblemsRussian Academy of SciencesMoscowRussia
  3. 3.LLC GeoGradStroiMoscowRussia
  4. 4.Siberian Chemical CombineLaboratory of Geotechnical MonitoringSeverskRussia

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