Release:
2025. Vol. 11. № 1 (41)About the authors:
Sergei I. Grachev, doctor of technical sciences, professor, head of the department of Oil and Gas Field Development, Tyumen Industrial University, Tyumen, RussiaAbstract:
It is known that at the main stages of the development of hydrocarbon deposits, it is necessary to promptly regulate oil production by using mathematical tools based on the physical principles of their development. They allow you to make management decisions with the required accuracy of the result. This is related to the use of analytical and simplified numerical models. The oil displacement characteristics evaluate the effectiveness of the flooding technology. Their use avoids the huge waste of time and financial resources that must be allocated to the process of three-dimensional hydrodynamic modeling. The proposed results of applying probable forecasting using integral curves of flooding compensate for the disadvantages of using displacement characteristics and the possibility of forecasting using strictly approved criteria for filtering improbable extrapolated values, which allows you to get the optimal result when the same input data.References:
Gavura A.V. et al. (2017). Engineering analysis of field development: Textbook. Moscow: Publishing Center of Gubkin Russian State University of Oil and Gas (NRU). [In Russian]
Kazakov A. A. (2003). Methodological support for unified approaches to evaluating the effectiveness of PNP methods // Tekhnologii toplivno-energeticheskogo kompleksa. (2), 47–53. [In Russian]
Kazakov A. A. (2020). Methods of characteristics of oil displacement by water. Nedra. [In Russian]
Mishchenko K. P & Tikhomirova E. A. (2022). Assessment of the predictive ability of oil displacement characteristics for operational analysis of field development indicators // Mezhdunarodnyj nauchno-issledovatel’skij zhurnal. 6, 158-163. https://doi.org/10.23670/IRJ.2022.120.6.023
Nazarenko M. Yu. & Zolotukhin A. B. (2020). Application of machine learning for probabilistic forecasting of production and calculation of potential recoverable oil reserves // Neftyanoe hozyajstvo. (9), 109–113. https://doi.org/10.24887/0028-2448-2020-9-109-113
Nazarenko M. Yu. & Zolotukhin A. B. (2021). Application of machine learning methods for probabilistic production forecasting. Nedra [In Russian]
Nalivkin V. D., Belonin M. D., Lazarev V. S. & Sverchkov G. P. (1981). Methodology for forecasting oil and gas reserves: achievements and prospects // Energiya i toplivo. (3), 96–101. [In Russian]
Ruchkin A. A. & Guseva D. N. (2016). A new approach to estimating recoverable reserves based on displacement characteristics // Neftepromyslovoe delo. (1), 43-47. [In Russian]
Ruchkin A. A. & Levagin S. A. (2016). Probabilistic forecast of recoverable reserves based on displacement characteristics // Proceedings of the International Scientific and Technical Conference. (pp. 393-401). Khanty-Mansiysk. [In Russian]
Sokolov S. V. (2014). Modification of displacement characteristics for the short-term forecast of oil production and assessment of the effect of the implementation of the program of geological and technical measures // Materials of the International scientific and technical conference. (pp. 284-288). Khanty–Mansiysk. [In Russian]
Sokolov S. V. (2016). A mathematical model for predicting basic oil production, taking into account uncertainties based on the method of displacement characteristics // Vestnik Tyumenskogo gosudarstvennogo universiteta. Fiziko-matematicheskoe modelirovanie. Neft’, gaz, energetika. 2(1), 82-91. https://doi.org/10.21684/2411-7978-2016-2-1-82-91
Khanipov M. N., Nasybullin A.V. & Sattarov R. Z. (2016). Probabilistic assessment of oil reserves involved in the development based on displacement characteristics using statistical methods // Neftyanoe hozyajstvo. (1), 37-39. https://doi.org/10.24887/0028-2448-2017-6-37-39
Kharisov M. N., Karpov A. A., Petrov S. V. & Darius S. D. (2018). An algorithm for determining the optimal displacement characteristics // Neftyanoe hozyajstvo. (5), 56-59. https://doi.org/10.24887/0028-2448-2018-5-56-59
Kharisov M. N., Kharisova E. A., Yunusova E. A. & Maisky R. A. (2018) Algorithm for determining displacement characteristics in conditions of data imperfection. // Neftegazovoe delo. 16(6), 20-25. https://doi.org/10.17122/ngdelo-2018-6-20-25
Shumko V. S. & Mamchistova E. I. (2020). The use of a probabilistic approach to assess potentially recoverable reserves // Materials of National Science and Technology. conferences. (pp. 144-147). Tyumen Industrial University. [In Russian]
Shumko V. S., Mamchistova E. I. & Kuzovlev S.S. (2021). Estimation of recoverable oil reserves using integral displacement characteristics based on probabilistic methodology // Izvestiya vysshih uchebnyh zavedenij. Neft’ i gaz. (2), 78-88. https://doi.org/10.31660/0445-0108-2021-2-78-88
Shumko V. S., Mamchistova E. I., Kolev J. M. & Gracheva S. K. (2022). Algorithmization of the forecast of recoverable oil reserves using a probabilistic technique based on displacement characteristics // Estestvennye i tekhnicheskie nauki. (3), 113–116. [In Russian]
Darwis S., Ruchjana B. N. & Permadi A. K. (2009). Robust decline curve analysis. Journal of the Indonesian Mathematical Society. 15(2), 105–111. [In English]
Paryani M., Ahmadi M., Awoleke O. & Hanks C. (2018). Decline curve analysis: a comparative study of proposed models using improved residual functions. Journal of petroleum & environmental biotechnology. 9(1), 1–8. https://doi.org/10.4172/2157-7463.1000362
Spivey J. P., Gatens J. M., Semmelbeck, M. E. & Lee W. J. (1992). SPE Mid-Continent Gas Symposium, 13–14 April, 1992. Amarillo, Texas. (pp. 91-100) [In English]
