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MINERAL GEOLOGY AND EXPLORATION
ArticleName Estimation of residual water saturation in 3D geological modeling
DOI 10.17580/em.2024.01.06
ArticleAuthor Strakhov P. N., Markelova A. A., Strakhova E. P.
ArticleAuthorData

Peoples Friendship University of Russia—RUDN, Moscow, Russia

Strakhov P. N., Professor
Markelova A. A., Laboratory Assistant, Researcher, markelova_aa@pfur.ru

 

Sergo Ordzhonikidze Russian State University for Geological Prospecting, Moscow, Russia
Strakhova E. P., Student, Laboratory Assistant

Abstract

This article discusses a new method of determining distributional patterns of residual water saturation in reservoirs during digital geological modeling. This is important for the construction of an oil saturation cube and prediction of character of stimulation of a hydrocarbon-bearing formation. Currently, construction of oil saturation cubes uses interpolation of borehole data. The reservoir properties of formations, which have influence on the nature of rock saturation, are neglected in this case. Considering essential scatter in the values of the study parameter and the comparatively large dimensions of the model cells, it is suggested to calculate histograms of residual water saturation coefficients. First, from the core testing data, the probability of non-exceedance of a certain critical water saturation value (80, 60, 40, 20) is calculated as function of porosity. For adapting the resultant dependences to larger objects, the unit cells are represented as sets of virtual rocks, with the sizes comparable with the sizes of core samples; the random number generator gives these rocks the values of reservoir properties such that the initial average values of porosity of the cells are preserved. For each type of conventional rock, the probability of nonexceedance of critical residual water saturation is calculated and the average values of the parameter are determined per cells. The relations between the probability of non-exceedance of critical water saturation and the porosity are approximated. Then, percentages of rocks within certain ranges of residual water saturation in a cell are determined. For the cube construction, the oil saturation index in an all-oil zone will be equal to the difference between one and residual water saturation, and will also depend on the height of rock occurrence above the water–oil contact in a water-and-oil zone.

keywords Reservoir, residual water saturation, porosity, reservoir properties, oil saturation index, core, rock, geological model
References

1. Varlamov A. I., Gogonenkov G. N., Melnikov P. N., Cheremisina E. N. Development of digital technologies in petroleum industry and subsoil use in Russia: Current state and future considerations. Russian Oil and Gas Geology. 2021. No. 3. pp. 5–2.
2. Khisamov R. S., Bachkov A. P., Voitovich S. E., Grunis E. G., Alekseev R. A. Artificial intelligence as important tool of modern geologist. Russian Oil and Gas Geology. 2021. No. 2. pp. 37–45.
3. Larue K., Allen J. P., Beeson D. Fluvial architecture and fourdimensional saturation modeling of a steam flood: Kern River field, California. Fluvial architecture and four-dimensional saturation modeling of a steam flood: Kern River field. D. California. AAPG Bulletin. 2020. Vol. 104(5). pp. 1167–1196.
4. Strakhov P. N., Koloskov V. N., Bogdanov O. A., Sapozhnikov A. B. Study of heterogeneities in oil and gas deposits. Moscow : Russian State University of Oil and Gas (NRU) named after I. M. Gubkin, 2018. 189. p.
5. Gilmanova R. Kh., Safiullin I. R., Rakhmatullin A. A., Belyaeva A. S. Modeling of hydrodynamic processes in the conditions of substandard reservoirs development by wells with hydraulic fracturing. Geology, Geophysics and Development of Oil and Gas Fields. 2022. No. 7. pp. 59–63.
6. Chen J., Pang X., Wang Y., Wang X. A new method for assessing tight oil, with application to the Lucaogou formation in the Jimusaer depression, Junggar basin, China. AAPG Bulletin. 2020. Vol. 104, No. 6. 1199–1229.
7. Sentsov A. Yu., Ryabov I. V., Ankudinov A. A., Radevich Yu. E., Polyakova N. S. et al. Analysis of the flooding system with application of statistical data processing methods. Oilfield Engineering. 2020. No. 8. pp. 5–9.
8. Kapustin N., Grushevenko D. Evaluation of long-term production capacity and prospects of the oil and gas industry of Russian Federation. E3S Web of Conferences. 2019. Vol. 114. ID 02001.
9. Ivanova M. M. Oil and gas field geology. Textbook of universities. Moscow : Nedra-Biznes center, 2000. 414 p.
10. Makhmutov A. A. Experience of oil extraction regulation from productive reservoirs of complex geological structure by non-stationary technologies application. Geology, Geophysics and Development of Oil and Gas Fields. 2022. No. 6. pp. 58–62.
11. Chernikov O. A. Studying geological nonuniformity of hydrocarbon reservoirs. Russian Oil and Gas Geology. 1995. No. 9. pp. 12–16.
12. Strakhov P. N., Belova A. A., Markelova A. A., Strakhova E. P. Accounting for productive deposits heterogeneity in geological modeling in order to improve an efficiency of water-alternated-gas injection. Oil Industry. 2021. No. 2. pp. 46–49.
13. Son Phan, Rima Chatterjee, Mrinal K. Sen, Triveni Gogoi. Predicting porosity, water saturation, and shale volume with high-resolution seismic inversion using Hopfield network. January AAPG Bulletin. 2022. Vol. 106, No. 1. pp. 103–118.
14. Salmachi A., Dunlop E., Rajabi M., Yarmohammadtooski Z., Begg S. Investigation of permeability change in ultradeep coal seams using time-lapse pressure transient analysis: A pilot project in the Cooper Basin, Australia. AAPG Bulletin. 2019. Vol. 103, No. 1. pp. 91–107.
15. Drozdov A. N., Drozdov N. A., Bunkin N. F., Kozlov V. A. Study of suppression of gas bubbles coalescence in the liquid for use in technologies of oil production and associated gas utilization. Society of Petroleum Engineers — SPE Russian Petroleum Technology Conference. 2017. ID 187741.
16. Das B., Chatterjee R. Porosity mapping from inversion of post-stack seismic data. Georesursy. 2016. Vol. 18, No. 4. P. 2. pp. 306–313.
17. Thomas Le Blévec, Olivier Dubrule, Cédric M. John, Gary J. Hampson. Geostatistical Earth modeling of cyclic depositional facies and diagenesis. Hampson. London. AAPG Bulletin. 2020. V. 104, No. 3. pp. 711–734.
18. Harun Ates, Asnul Bahar, Salem El-Abd, Mohsen Charfeddine, Mohan Kelkar et al. Ranking and upscaling of geostatistical reservoir models using streamline simulation: A Field case study. Reservoir Evolution & Engineering. 2005. Vol. 8, Iss. 01. pp. 22–32.
19. Komisarenko A. S., Kochetov A. V., Zagorovskii A. A., Kuznetsov E. G., Fedortsov I. V. Cavernous-fractured carbonate reservoir: problem of water–oil displacement ratio substantiation. Russian Oil and Gas Geology. 2021. No. 4. pp. 51–57.
20. Ashmyan K. D., Volpin S. G., Kovaleva O. V., Ponomarev A. K., Chen-Len-Son Yu. B. Zonal distribution of residual oil reserves in a productive reservoir. Geology, Geophysics and Development of Oil and Gas Fields. 2022. No. 8. pp. 56–61.

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