The Method of Choosing Optimal Implementations for Multivariate Digital 3D Modeling of Oil Deposits

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Abstract

Multivariate geological 3D modeling allows the most reliable consideration of the degree of uncertainty in the structure of oil deposits. At the same time, the task of selecting optimal settings for multivariate 3D modeling seems relevant, which involves the selection of criteria that limit the most probable implementations of the geological structure in the interwell space based on the uncertainty and existing heterogeneity of the geological environment. As a result, a model of reservoir distribution at each point in 3D space should be built that best matches the actual data. To study the heterogeneity of the geological structure of deposits, the work uses mathematical analysis methods based on experiment planning. A methodology for generating a set of lithological cubes with different variance parameters is implemented. The problem of optimizing the selection of geological 3D models is reduced to probabilistic ranking based on the criteria of their convergence with the results of well drilling, interpretation data of 3D seismic exploration, actual well operation and lithological continuity of reservoirs. A comprehensive accounting of these criteria based on the additive formula allows calculating the extremum (Qmax) with maximization of the ratio between the useful signal of the optimal response and interference. The possibilities of practical use of the developed methodology of geological multivariate 3D modeling are shown on the example of estimating the reserves of a real oil deposit. The step and ranges of change in the variogram ranks are consistently substantiated, after which 289 realizations of the geological 3D model are obtained by the kriging method. After calculating the complex criterion Qmax 49 3D models are characterized by a value of more than 0.9, which allows narrowing the samples of realizations. To obtain the final optimal version of 3D modeling, all realizations with Qmax > 0,9 at each point of the three-dimensional lithology cubes are averaged. Analysis of the generated realizations shows that due to the low density of the well grid over the area of the deposit, the 3D models do not correlate well enough with the actual drilling results. In such conditions, the model quality is largely determined by the reliability of 3D seismic data, which is much better implemented for 3D models with Qmax > 0.9. Models with low Qmax values do not reflect the lithological consistency of the reservoir area, as a consequence, the dynamics of oil production at the site. The practical result of the studies performed was the selection of the most realistic geological 3D model based on multiple implementations.

Keywords

3D geological modelingoil reserveskriginggeostatistical methodsvariogram analysisexperimental design.

Bibliography

  1. 1.Riabchevskikh, M.Iu., Nekrasov A.S. Kontseptual'naia geologo-gidrodinamicheskaia model' famensko-turneiskoi zalezhki kak instrument prognozirovaniia effektivnosti sistemy podderzhania plastovogo davleniia na primere mestorozhdeniia im. Sukhareva [Conceptual Geological and Hydrodynamic Model of the Famennian-Tournaisian Reservoir as a Tool for Predicting the Effectiveness of the Reservoir Pressure Surname System, using the Example of the Sukhareva Field]. Nedropol'zovanie, 2023, no. 23 (2), pp. 58-70. DOI: 10.15593/2712-8008/2023.2.2 Scholar
  2. 2.Li H., Zhang J. Well log and seismic data analysis for complex pore-structure carbonate reservoir using 3D rock physics templates. Journal of Applied Geophysics, 2018, no. 151, pp. 175-183. DOI: 10.1016/j.jappgeo.2018.02.017 Scholar
  3. 3.Voloskov D.S., Koroteev D.A. Postroenie adaptivnykh gidrodinamicheskikh modeli ponizhennogo poriadka na osnove metoda POD-DEIM [Construction of adaptive reduced-order reservoir models based on POD-DEIM approach]. Geochemistry, 2023, no. 25 (4), pp. 69-81. DOI: 10.18599/ge.2023.4.4 Scholar
  4. 4.Isakova T.G., D'iakonova T.F., Nosikova A.D., Kalmykov G.A., Akin'shin A.V., Iatsenko V.M. Prognoznaia otsenka fil'tratsionnoi sposobnosti tonkosolistykh kollektorov vikulovskoi syiy po rezultat'am issledovaniia kerna i GIS [Predictive assessment of the fluid loss properties of thin-layer reservoirs of Vikulovskaya series based on the results of core and well logs]. Georesursy, 2021, no. 23 (2), pp. 170-178. DOI: 10.18599/gsr.2021.2.17 Scholar
  5. 5.Nikanorova M.A., Korolev E.S., Shapovalov M.Iu., Kalinin E.N., Shadchnev N.A., Babinov K.Iu. Utochnenie geologicheskoi modeli iurskikh otlozhenii s uchetom rezultat'ostokhsticheskoi inversii i fastial'nogo modelirovaniia [Refinement of the geological model of Jurassic deposits accounting the results of stochastic inversion and facies modeling]. Georesursy, 2023, no. 25 (3), pp. 57-64. DOI: 10.18599/grs.2023.3.8 Scholar
  6. 6.Yang P., Sun Z., Liang X., Li H., Dan G. Seismic strategy for predicting highly profitable wells in the fractured-vuggy carbonate reservoirs. Petroleum Exploration and Development, 2013, no. 40 (4), pp. 537-541. Scholar
  7. 7.Iturrarán-Viveros U., Parra J.O. Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. Journal of Applied Geophysics, 2014, no. 107, pp. 45-54. Scholar
  8. 8.Qi C., Wang X., Wang W., Liua J., Tuo J., Liu K. Three-dimensional characterization of micro-fractures in shale reservoir rocks. Petroleum Research, 2018, no. 3 (3), pp. 259-268. DOI: 10.1016/j.ptlrs.2018.08.003 Scholar
  9. 9.Tolstukhin E., Barrela E., Khrulenko A.A., Halotel J., Demyanov V.V. Ensemble history matching enhanced with data analytics – a Brown field study. Petroleum Geostatistics, 2019, vol. 2019, pp. 1-5. DOI: 10.3997/2214-4609.201902201 Scholar
  10. 10.Agzamov A., Efendiyev G., Moldabayeva G.Zh., Syzdykov A., Suleimenova R., Tuzelbayeva Sh., Zaurbekov К. On the degree of influence of waterflooding on the oil recovery factor from productive formations of high-viscosity reservoirs X. Complex Use of Mineral Resources, 2023, vol. 326 (3), pp. 50-58. DOI: 10.31643/2023/6445.28. Scholar

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Published
24 October 2025
Issue date
24 October 2025
Issue
Vol. 25, No. 3 (2025)
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