Interest in putting Achimov deposits into development creates new challenges in terms of geological modeling. The experience of appraisal and exploitation drilling in these reservoirs has shown that one of the key risks is associated with the complex distribution of fluids across productive horizons. This distribution indicates the presence of isolated lenses, the scale of which is often lower than seismic resolution. Applying most common modeling algorithms for high net-to-gross reservoirs leads to creation of models with very high connectivity, which is usually not typical for turbidite deposits owning to the features of sedimentary processes. The authors analyze the most common 3D modeling algorithms in the context of applicability for the probabilistic assessment of Achimov deposits using the example of Ach0-Ach4 formations of Zapadno-Pestsovoye Field. For each of the methods considered advantages and limitations are given. It is concluded that to solve the problem at the current stage of the project for the studied reservoirs the object method is optimal. It allowed considering many alternative options regarding the size of isolated sandbodies and their saturation with oil, gas and water. Based on the results of the assessment it was revealed that the P10/P90 ratio for oil-in-place in each lens penetrated by wells ranges from 6 to 8 and only about 20% of potentially productive sandbodies is discovered, which indicates a very low exploration maturity and high operational drilling risks. The use of a similar approach for probabilistic geological assessment at appraisal stage is also possible for reservoirs formed in other sedimentary processes, characterized with low connectivity and risks of fluid saturation of isolated sandbodies.
References
1. Bukatov M.V., Peskova D.N., Nenasheva M.G. et al., Key problems of Achimov deposits development on the different scales of studying (In Russ.), Proneft'. Professional'no o nefti, 2018, no. 2, pp. 16–21, DOI: https://doi.org/10.24887/2587-7399-2018-2-16-21
2. Pleshanov N.N., Peskova D.N., Zaboeva A.A. et al., Complex analysis of factors that influenced on water saturation forecast of Achimov formation at Gazpromneft licence blocks (In Russ.), PROneft'. Professional'no o nefti, 2020, no. 3(17), pp. 16–25, DOI: https://doi.org/10.7868/S2587739920030027
3. Manzocchi T., Walsh D.A., López-Cabrera J. et al., Compression-based modelling honouring facies connectivity in diverse geological systems, Springer Proceedings in Earth and Environmental Sciences: Geostatistics Toronto 2021 – Quantitative Geology and Geostatistics, 2023, pp. 111–117, DOI: http://doi.org/10.1007/978-3-031-19845-8_8
4. Natchuk N.Yu., Monakhova V.O., Pakhomov S.I. et al., Methodology and practice of analysis of geological models uncertainty of Achimov deposits on the example of the Urengoi field (In Russ.), Нефтепромысловое дело, 2019, no. 11(611), pp. 15–25, DOI: https://doi.org/10.30713/0207-2351-2019-11(611)-15-25
5. Prélat A., Covault J.A., Hodgson D.M. et al., Intrinsic controls on the range of volumes, morphologies, and dimensions of submarine lobes, Sedimentary Geology, 2010, no. 1–2(232), pp. 66–76, DOI: https://doi.org/10.1016/J.SEDGEO.2010.09.010
6. Deutsch C., Geostatistical reservoir modeling, Oxford: Oxford University Press, 2002, 392 p.
7. Kovalevskiy E.V., Geologicheskoe modelirovanie na osnove geostatistiki (Geological modeling based on geostatistics), Moscow: Publ. of EAGE, 2011, 117 p.
8. Zhang L., Pan M., Li Z., 3D modeling of deepwater turbidite lobes: a review of the research status and progress, Petroleum Science, 2020, no. 17, pp. 317–333,
DOI: http://doi.org/10.1007/s12182-019-00415-y
9. Pyrcz M.J., Sech R.P., Covault J.A. et al., Stratigraphic rule-based reservoir modeling, Bulletin of Canadian Petroleum Geology, 2015, no. 4(63), pp. 287–303,
DOI: http://doi.org/10.2113/gscpgbull.63.4.287
10. Walsh D.A., Manzocchi T., A method for generating geomodels conditioned to well data with high net:gross ratios but low connectivity, Marine and Petroleum Geology, 2021, V.129, DOI: https://doi.org/10.1016/J.MARPETGEO.2021.105104
11. Rose P.R., Risk analysis and management of petroleum exploration ventures, Tulsa: American Association of Petroleum Geologists, 2001, 164 p.
12. Popova O.A., Influence of correlations on results of probabilistic geological modelling (In Russ.), Нефтегазовая геология. Теория и практика, 2020, no. 3. –
