Method for rapid selection of candidate wells for conversion to injection based on the CRMP material balance model

About the author:

Alexander D. Bekman, Cand. Sci. (Phys.-Math.), Chief Project Engineer, LLC “RN – Geology Research Development”, Tyumen, Russia

adbekman@rn-gir.rosneft.ru, https://orcid.org/0000-0002-5907-523X

Abstract:

Selecting candidate wells for conversion to injection, as well as choosing the appropriate moment for conversion of a specific well, are pressing issues in oil and gas field development analysis. Solving such problems using traditional 3D reservoir simulation models (RSM) is not theoretically difficult; however, in practice, it can require unacceptably long calculation times. Various proxy models are currently used to quickly solve many development analysis problems; though, the methods for their use for this purpose have not been sufficiently developed. Specifically, the difficulty of using such models to select candidate wells for conversion to injection is due to the lack of estimates for the coefficients of interaction between a new injection well and producing wells, which makes it impossible to estimate predicted oil flow rates. This article proposes a method for estimating such coefficients, enabling the calculation of a forecasted oil flow rate using the well-known capacitance-resistance model (CRMP). The results of numerical experiments to evaluate the effectiveness of the new method on a synthetic RSM are presented.

References:

Afanaskin, I. V., Kolevatov, A. A., Glushakov, A. A., & Yalov, P. V. (2023). Consideration of non-linear deformation of the void space in the CRM-model when analyzing the development of a gas deposit for depletion. Geology, Geophysics and Development of Oil and Gas Fields, (2), 59–65. https://doi.org/10.33285/2413-5011-2023-2(374)-59-65 [In Russian]

Afanaskin, I. V., Kryganov, P. V., Glushakov, A. A., & Yalov, P. V. (2020). CRM well interference models for evaluating reservoir filtration and volumetric properties from production data. Russian Journal of Cybernetics, 1(1), 17–27. https://doi.org/10.51790/2712-9942-2020-1-1-3 [In Russian]

Bekman, A. D. (2023). Accounting for stimulation treatments in modeling of oil reservoirs development using the material balance method. Mathematical Models and Computer Simulations, 15, 13–22. https://doi.org/10.1134/S2070048223010027

Bekman, A. D. (2025). A new method of oil production rates forecasting for producers in sub-gas zones based on CRM. Journal of Information Technologies and Computing Systems, (1), 106–115. [In Russian]

Bekman, A. D., Pospelova, T. A., & Zelenin, D. V. (2020). A new approach to water cut forecasting based on results of capacitance resistance modeling. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 6(1), 192–207. https://doi.org/10.21684/2411-7978-2020-6-1-192-207 [In Russian]

Bekman, A. D., & Ruchkin, A. A. (2024). Method for assessing well interference at under-gas cap zone using CRM material balance model. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 10(1), 155–173. https://doi.org/10.21684/2411-7978-2024-10-1-155-173 [In Russian]

Bekman, A. D., Stepanov, S. V., Ruchkin, A. A., & Zelenin, D. V. (2019). A new algorithm for finding CRM-model coefficients. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 5(3), 164–185. https://doi.org/10.21684/2411-7978-2019-5-3-164-185 [In Russian]

Danko, M. Yu., Brilliant, L. S., & Zavyzlov, A. S. (2019). Application of dynamic material balance method and CRM method (capacitance-resistive models) for reserves assessment in Achimov and Bazhenov reservoirs. 21st Century Subsoil Use, (4), 76–85. EDN CTHJSE. [In Russian]

Zelenin, D. V., Stepanov, S. V., Bekman, A. D., & Ruchkin, A. A. (2019). Research of mechanisms for accounting of wells interaction when using various methods of mathematical modeling. Oilfield Engineering, (12), 39–45. https://doi.org/10.30713/0207-2351-2019-12(612)-39-45 [In Russian]

Lebedev V. I., Musakaev E. N., Musakaev N. G., & Rodionov S. P. (2023). Integrated model “reservoir-well” based on D-CRMP. Oil and Gas Studies, (6), 35-46. https://doi.org/10.31660/0445-0108-2023-6-11-35-46 [In Russian]

Pospelova, T. A., Zelenin, D. V., Zhukov, M. S., Bekman, A. D., & Ruchkin, A. A. (2020). Water cut system optimization based on CRM model. Oilfield Engineering, (7), 5–10. https://doi.org/10.30713/0207-2351-2020-7(619)-5-10 [In Russian]

Stepanov, S. V., Bekman, A. D., Ruchkin, A. A., & Pospelova, T. A. (2021). Oil Reservoir Management Methods with the Use of Capacitance Resistive Models. Express. [In Russian]

Stepanov, S. V., Sokolov, S. V., Ruchkin, A. A., Stepanov, A. V., Knyazev, A. V., & Korytov, A. V. (2018). Considerations on mathematical modeling of producer-injector interference. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 4(3), 146–164. https://doi.org/10.21684/2411-7978-2018-4-3-146-164 [In Russian]

Stepanov, S. V., Stepanov, A. V., & Eletsky, S. V. (2013). Numerical-analytical approach towards salvation of the problem relating to on-line prediction of an oil well operation in conditions of a gas cone formation. Oilfield Engineering, (2), 53-58. [In Russian]

Shevtsov, N. O. (2023). Modification of the capacitance resistive model (CRM) for solving optimization problems of oil fields development. Oilfield Engineering, (11), 20–27. https://doi.org/10.33285/0207-2351-2023-11(659)-20-27 [In Russian]

Shevtsov, N. O., & Stepanov, S. V. (2024). Research of the relationship of wells interaction coefficients in the CRM with the hydraulic conductivity field within the framework of a hierarchical modeling approach. Oilfield Engineering, (7), 20–25. [In Russian]

Shevtsov, N. O., Stepanov, S. V., & Pospelova, T. A. (2020). The study of the predictive ability of numerical and analytical models (the case of mutual well impact evaluation). Tyumen State University Herald. Phys. and Mathematics Modeling. Oil, Gas, Energy, 6(3), 131–142. https://doi.org/10.21684/2411-7978-2020-6-3-131-142 [In Russian]

Bahrami, P., Sahari Moghaddam, F., & James, L.A. (2022). A Review of proxy modeling highlighting applications for reservoir engineering. Energies, 15(14), 5247. https://doi.org/10.3390/en15145247

Cao, F. (2014). Development of a Two-Phase Flow Coupled Capacitance Resistance Model [Ph.D. Dissertation]. University of Texas.

de Holanda, R. W., Gildin, E., Jensen, J. L., Lake, L. W., & Kabir, C. S. (2018). A state-of-the-art literature review on capacitance-resistance models for reservoir characterization and performance forecasting. Energies, 11(12), 3368. https://doi.org/10.3390/en11123368

Gentil, P. H. (2005). The Use of Multilinear Regression Models in Patterned Waterfloods: Physical Meaning of the Regression Coefficients [M. S. Thesis]. University of Texas.

Gubanova, A. E., Khabibullin, B. A., Orlov, D. M., & Koroteev, D. A. (2021). Modified CR-type material balance model for well production forecasts in case of well treatments. In Society of Petroleum Engineers — SPE Russian Petroleum Technology Conference 2021. https://doi.org/10.2118/SPE-206511-MS

Sayarpour, M. (2008). Development and Application of Capacitance-Resistive Models to Water/CO2 Floods [Ph. D. Dissertation]. University of Texas.

Sayarpour, M., Zuluaga, E., Kabir, C. S., & Lake, L. W. (2009). The use of capacitance-resistance models for rapid estimation of waterflood performance and optimization. Journal of Petroleum Science and Engineering, 69(3–4), 227–238. https://doi.org/10.1016/j.petrol.2009.09.006

Yousef, A. A., Gentil, P. H., Jensen, J. L., & Lake, L. W. (2006). A capacitance model to infer interwell connectivity from production- and injection-rate fluctuations. SPE Reservoir Evaluation & Engineering, 9(6), 630–646. https://doi.org/10.2118/95322-PA