Study of the predictive ability of the CRM analytical material balance model as a part of a retrospective test on a real field

About the authors:

Nikita O. Shevtsov, Postgraduate Student, Department of Applied and Technical Physics, University of Tyumen; Specialist, Tyumen Petroleum Research Center; noshevtsov@tnnc.rosneft.ru

Aleksandr V. Korytov, Section Head, Tyumen Petroleum Research Center; avkorytov2@tnnc.rosneft.ru

Abstract:

For solving the problems of operational control and optimization of the waterflooding system, the choice of a model should be based on an understanding of its predictive ability. The article obtained the results of assessing the predictive ability of the CRM analytical material balance model in the framework of a retrospective test at a real field site. In addition to the single-phase representation, classical for the CRM model, special attention is paid to the predictive qualities of the two-phase formulations of the model. Based on the test results, it is shown that the CRM model in a two-phase setting allows predicting oil production at a high level of accuracy with detailing to waterflooding elements.

References:

1.  Stepanov S. V., Stepanov A. V., Beckman A. D., Ruchkin A. A. 2019. Certificate of registration of the program for the ECM 2019661608 “Ariadna”. No. 2019660439;
declared 21 August 2019; published 04 September 2019. [In Russian]

2.   Nekhoroshkova A. A., Danko M. Yu., Savyalov A. S., Olisheva A. O. 2019. “Critical analysis of the INSIM-FT (Interwell numerical simulation front tracking models) proxy modeling method”. Oil. Gas. Innovations, no. 12 (229), pp. 49-55. [In Russian]

3.   Beckman A. D., Pospelova T. A., Zelenin D. V. 2020. “A new method for predicting well water cut dynamics using CRMP modeling results”. Tyumen State University
Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 6, no. 1 (21),
pp. 192-207. [In Russian]

4.   Olenchikov D., Posvyanskii D. 2019. “Application of CRM-like models for express forecasting and optimizing field development”. Russian Petroleum Technology Conference. Moscow, Russia.

5.   Sayarpour M. 2008. “Development and application of capacitance-resistive models to water/CO2 floods”. Dr. Sci. diss. Austin, TX, USA: University of Texas.

6.   Zhang T., Izgec O., Sayarpour M. 2018. “Heavy oil waterflood application of capacitance resistance models”. SPE Annual Technical Conference and Exhibition. Dallas, Texas, USA.

7.   Yrigoyen A., Saputelli L., Chacon A., Chegin J. 2017. “Identifying cost-effective waterflooding optimization opportunities in mature reservoirs from data driven analytics”. SPE Annual Technical Conference and Exhibition. San Antonio, Texas, USA.

8.   Holanda R. W., Gildin E., Jensen J. L. 2015. “Improved waterflood analysis using the capacitance-resistance model within a control systems framework”. SPE Latin American and Caribbean Petroleum Engineering Conference. Quito, Ecuador.

9.   Evseenkov A. S., Kuchkildin D. K., Krechetov K. I. [et al.] 2021. “Short-term forecasting of well production based on a hybrid probabilistic approach”. Society of Petroleum Engineers. SPE Russian Petroleum Technology Conference 2021. RPTC 2021. Virtual, Online. DOI: 10.2118/SPE-206519-MS