The influence of geological and technological factors on the probability of determining zones of residual oil reserves
https://doi.org/10.31660/0445-0108-2019-5-46-56
Abstract
This article is devoted to the probability maps have been constructed for predicting the zones of residual oil reserves using the example of deposits in Shaim region. The refinement of the previously presented algorithm [4] has been made, which helps with a fairly high degree of probability to quickly localize the residual oil reserves based on 2D modeling. In the process of work, the influence of many geological and technological parameters on the final probability map was established, the influence of the observation zone on the value of the correlation coefficient of the map of residual mobile oil reserves with the map of current mobile oil reserves based on geological and hydrodynamic modeling was established.
About the Author
E. S. AzarovRussian Federation
Evgeny S. Azarov, Chief Specialist
Tyumen
References
1. Huseynova, D. F. (2016). Oil field drainage efficiency identification. Neftegazovoye delo,15(2), рр. 50-54. (In Russian).
2. Voronova, E. V. (2006). Sozdaniye metodiki otsenki ostatochnykh zapasov nefti na mestorozhdeniyakh, nakhodyashchikhsya na zavershayushchey stadia razrabotki. Aktual'nyye problem neftegazovogo dela. Sbornik nauchnykh trudov, tom 1, рр. 26-30. (In Russian).
3. Zaydullin, A. I. & Voronova, E. V. (2004). Razrabotka i vnedreniye programmy analiza i approksimatsii mnogofaktornykh svyazey na primere geologo-tekhnicheskikh dannykh. Problemy razrabotki i ekspluatatsii neftyanykh mestorozhdeniy: mezhvuzovskiy sbornik nauchnykh tru-dov, рр. 391-396. (In Russian).
4. Kondratev, M. A. & Azarov, E. S. (2018). Probability approach as a tool for detectingzones of oil residual reserves. Oilfield Engineering, (10), рр. 12-19. (In Russian).
5. Dubrule, O. (2003). Geostatistics for Seismic Data Integration in Earth Models. EAGE, 296 p. (In English).
6. Johnson, M. E. (1987). Multivariate Statistical Simulation, Wiley Series in Probability and Mathematical Statistics, 230 p. (In English).
7. Kondratiev, M. A. & Azarov, E. S. (2018). Probabilistic Approach as a Tool for Identifying Areas of Residual Oil Reserves, SPE-187925-MS, 11 p. (In English).
8. Kuznetsov, D. V., Kuleshov, V. E., & Mogutov, A. S. (2013). Podschet zapasov nefti i rastvorennogo gaza. Ukhta, Ukhta State Technical University Publ., рр. 21-33. (In Russian)
9. Kostin, V. N. & Tishina, N. A. (2004). Statisticheskiye metody i modeli. Orenburg, Orenburg State University Publ., 138 p. (In Russian).
10. Marchuk, V. I. & Tokareva, S. V. (2009). Sposoby obnaruzheniya anomal'nykh znacheniy pri analize nestatsionarnykh sluchaynykh protsessov. Shakhty, 210 p. (In Russian).
11. Pustyl'nik, E. I. (1968). Statisticheskiye metody analiza i obrabotki nablyudeniy. Moscow, Nauka Publ., 288 p. (In Russian).
12. Thompson, W. R. (1962). The problem of negative estimates of variance components. Annals of mathematical statistics, 33(1), рр. 273-289. (In English).
13. David Garson, G. (1979). Ordinal Regression. 336 p. (In English)
14. Nizaev, R. Kh., & Sudo, R. M. (2016). Principles of mobile oil distribution mapping. Oil and Gas Territory, (3), pp. 126-130. (In Russian).
15. Gimatudinov, Sh. K. (1971). Fizika neftyanogo i gazovogo plasta. Moscow, Nedra Publ., 312 p. (In Russian).
16. Mikhaylov, N. N. (1992). Ostatochnoye neftenasyshcheniye razrabatyvayemykh plastov. Moscow, Nedra Publ., 270 p. (In Russian).
17. Larson, R. G., Davis, H. T. & Scriven, L. E. (1981). Displacement of residual nonwetting fluid from porous media. Chemical Engineering Science, 36(1), рр. 75-85. (In English). DOI: 10.1016/0009-2509(81)80049-8
18. Petrov, V. N. (2015). Optimization of heterogeneous reservoir development by the example of terrigenous formation D1 of Abdrakhmanovskaya area, Romashkinskoye oil field. Oil and Gas Territory, (11), рр.113-117. (In Russian).
Review
For citations:
Azarov E.S. The influence of geological and technological factors on the probability of determining zones of residual oil reserves. Oil and Gas Studies. 2019;(5):46-56. (In Russ.) https://doi.org/10.31660/0445-0108-2019-5-46-56