Main features of modeling terrigenous deposits in East-Siberia using the example of the Hamakinsky horizon
https://doi.org/10.51890/2587-7399-2021-6-4-32-42
Abstract
Materials and methods. Conceptual sedimentary model bases on the core of the 14 wells. Updating of the petrophysical model is the key to identify post-sedimentary transformations (like anhydritization and halitization) and the opportunity to correct the permeability trend. The tectonic pattern of the horizon based on the interpretation of 3D seismic data. There are two groups of faults were identified: certain and possible. Neural networks algorithm uses for a creating the predictive maps of anhydritization, which are used in the geological model.
Results. Estuary sands influenced by fluvial and tidal processes dominate the khamakinskii horizon. The reservoir is irregular vertically: at the base of the horizon, there are sandstones of the delta front and there are alluvial valley with fluvial channels in the middle and upper parts. Eustary sands eroded by incised valleys (alluvial channels). According to the core and thin section analysis, the main uncertainty is sedimentary transformations of reservoir. It affects the net thickness and then the volume of oil in productive wells. 3D geological model includes the trends of anhydritization and halitization over the area, which makes it possible to obtain a more accurate production forecast.
Conclusion. As part of the probability estimate of oil reserves, the main geological parameters that affect the volume of reserves were identified. Pilot project is planning to remove geological and technical uncertainties.
About the Authors
Dmitriy V. KozikovRussian Federation
Mikhail A. Vasiliev
Russian Federation
Konstantin V. Zverev
Russian Federation
Andrei N. Lanin
Russian Federation
Shafkat A. Nigamatov
Russian Federation
Sergey A. Andronov
Russian Federation
References
1. Мельников Н.В. Венд-кембрийский соленосный бассейн Сибирской платформы (Стратиграфия, история развития). — Новосибирск: Изд-во СО РАН, 2009. — 148 с.
2. Постникова О.В. Палеогеографические и палеогеодинамические условия формирования рифей-вендского осадочного бассейна юга Сибирской платформы в связи с его нефтегазоносностью // Геология нефти и газа. — 2008. — № 1. — С. 8–15.
3. Нигаматов Ш.А., Исмагилова Л.Р., Бощенко А.Н. Прогноз зон засолонения песчаников ботуобинского горизонта на примере Чаяндинского месторождения (Восточная Сибирь) // PROНЕФТЬ. Профессионально о нефти. — 2019. — № 3 (13). — С. 35–40.
4. Kohonen T. Self-organizing maps: Springer-Verlag. — New York, Inc., 1995.
5. Neff D.B., S.A. Runnestrand and E.L. Butler Multi-attribute seismic waveform classification, Phillips Petroleum Company, USA Patent 6223126, 2001.
6. Priezzhev I., Shmaryan L., Bejarano G. Non-linear multi trace seismic inversion using neural network and genetic algorithm — “Genetic Inversion”: Annual Meeting St Petersburg. EAGE, Extended Abstracts, 2008.
7. Priezzhev I., Scollard A., Lu Z. Regional production prediction technology based on gravity and magnetic data from the Eagle Ford formation. — Texas, USA, Denver SEG, 2014.
8. Tikhonov A.N. and Arsenin V.Y. Solutions of ill-posed problems. Washington D.C., V H Winston and Sons, 1977.
9. Priezzhev I.I., Veeken P.Ch., Egorov S.V., Strecker U. Direct prediction of petrophysical and petroelastic reservoir properties from seismic and well-log data using nonlinear machine learning algorithms // The Leading Edge. — 2019. — Vol. 38. — № 12. — p. 949–958.
10. Strecker U., Mahmoud I., Priezzhiev I., Zeug M. Chancing methods to predict porosity in a middle eastern carbonate reservoir from full-function machine-learning neural networks, seismic attributes and inversions // AAPG GeoTechnology Workshop. — Abu Dhabi, January 28–29, 2020.
11. Приезжев И.И. Нейронные сети нового поколения на основе теоремы Колмогорова и их применение для прогнозно-инверсионных построений // ГеоЕвразия 2020. 3–5 февраля 2020, Москва.
12. Приезжев И.И., Васильев М.А., Петренко Е.Н. Построение прогнозных карт эффективных газонасыщенных толщин по форме сейсмического сигнала на основе нейронных сетей Кохонена // Геофизика. — 2020. — № 6. — С. 40–44.
Review
For citations:
Kozikov D.V., Vasiliev M.A., Zverev K.V., Lanin A.N., Nigamatov Sh.A., Andronov S.A. Main features of modeling terrigenous deposits in East-Siberia using the example of the Hamakinsky horizon. PROneft. Professionally about Oil. 2021;6(4):32-42. (In Russ.) https://doi.org/10.51890/2587-7399-2021-6-4-32-42