New method for splitting production and injection in joint wells using modified CRM model

About the author:

Abstract:

Operating several oil-bearing facilities with a single grid of wells, the problem of dividing oil and liquid production rates by facilities is urgent. Known engineering techniques based on reservoir transmissibility coefficients and effective oil-saturated thickness do not take into account dynamic factors. The use of hydrodynamic models (HDM) is time-consuming, and the results depend significantly on the used a priori hypotheses about the geological structure of objects and the properties of fluids. Thus, there is a practical need for an analytical tool that would rely on the most reliable and available data and would allow solving the problem of separating the volumes of produced fluid and injected water with sufficient accuracy. Such a tool should take into account the dynamics of changes in reservoir pressure and have a low (compared to the hydrodynamic model) need for computing resources. A promising candidate for the role of such a tool is the CRMP-ML6 model — a fundamentally new author’s modification of the previously known CRMP model.

The CRMP model is a functional dependence of the well fluid flow rate on the injectivity of the surrounding injection wells. The unknown parameters of this dependence are determined in such a way as to minimize the discrepancy between the simulated and actual values of production rates at the selected date interval. Fundamentally new features of the CRMP-ML6 model are the regularization of the problem through the use of a priori information on the permeability of reservoirs in the vicinity of production wells and the requirement for the proximity of reservoir pressures calculated using the material balance model and from the Dupuis equation.

To assess the performance of the new model, a number of numerical simulation experiments were carried out, and the simulation results were compared with the HDM. The possibility of the CRMP-ML6 model is demonstrated to take into account the dynamic separation of production and injection, taking into account additional constraints and a priori information, and while meeting all the requirements for models of the CRM family.

References:

1. Altunin A. E., Semukhin M. V., Stepanov S. V. 2012. “Use of material balance and fuzzy set theory to solve the problem of production separation while developing several layers”. Oil industry, no. 5, pp. 56-60. [In Russian]

2. Beckman A. D. 2021. Data for verifying the performance of the CRMP-ML6 model. https://github.com/MaxFloat/CRMP-ML6_verification [In Russian]

3. Beckman A. D., Stepanov S. V., Ruchkin A. A., Zelenin D. V. 2019. “A new algorithm for finding the optimal solution to the problem of determining the coefficients of mutual influence of wells within the framework of the CRM model”. Tyumen State University Herald. Physical and mathematical modeling. Oil, gas, energy, vol. 5, no. 3, pp. 164-185. DOI: 10.21684/2411-7978-2019-5-3-164-185 [In Russian]

4. Blinov A. F., Diyashev R. N. 1971. Exploration of jointly-exploited reservoirs. Moscow: Nedra. 176 p. [In Russian]

5. Pospelova T. A., Zelenin D. V., Zhukov M. S., Beckman A. D., Ruchkin A. A. 2020. “Optimization of the waterflooding system based on the CRM model”. Oilfield business, no. 7, pp. 5-10. [In Russian]

6. Stepanov S. V., Ruchkin A. A., Stepanov A. V. 2018. “Analytical method for separating liquid and oil production by formations during their joint development”. Oilfield business, no. 2, pp. 10-17. [In Russian]

7. Stepanov S. V., Vasiliev V. V., Altunin A. E. 2015. “An improved analytical method for separating production and injection into layers while simultaneously developing them together”. Oil industry, no. 11, pp. 27-31. [In Russian]

8. 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. https://www.mdpi.com/1996-1073/11/12/3368/html

9. Mamghaderi A., Bastami A., Pourafshary P. 2013. “Optimization of waterflooding performance in a layered reservoir using a combination of capacitance-resistive model and genetic algorithm method”. Journal of Energy Resources Technology, vol. 135, iss. 1, art. 013102. https://asmedigitalcollection.asme.org/energyresources/article-abstract/135/1/013102/368098/Optimiza.... DOI: 10.1115/1.4007767

10. Mamghaderi A, Pourafshary P. 2013. “Water flooding performance prediction in layered reservoirs using improved capacitance-resistive model”. Journal of Petroleum Science and Engineering, vol. 108, pp. 107-117.

11. Moreno G. A. 2013. “Multilayer capacitance-resistance model with dynamic connectivities”. Journal of Petroleum Science and Engineering, vol. 109, pр. 298-307.

12. Sayarpour M. 2008. “Development and application of capacitance-resistive models to water/CO2 floods”. Dr. Sci. (Philos.) diss. The University of Texas at Austin. https://www.researchgate.net/publication/280579098_Development_and_Application_of_Capacitance-Resistive_Models_to_WaterCO2_Floods