Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)

For citation:

Persova M. G., Soloveichik Y. G., Patrushev I. I., Nasybullin A. V., Altynbekova G. Z., Leonovich D. A. Optimization of oil field development based on a 3D reservoir model obtained as a result of history matching. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2023, vol. 23, iss. 4, pp. 544-558. DOI: 10.18500/1816-9791-2023-23-4-544-558, EDN: XGVLMB

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
Full text:
(downloads: 394)
Article type: 

Optimization of oil field development based on a 3D reservoir model obtained as a result of history matching

Persova Marina G., Novosibirsk State Technical University
Soloveichik Yuri G., Novosibirsk State Technical University
Patrushev Ilya Igorevich, Novosibirsk State Technical University
Nasybullin Arslan V., Almetyevsk State Oil Institute
Altynbekova Gulayym Zh., Novosibirsk State Technical University
Leonovich Daryana A., Novosibirsk State Technical University

The paper proposes an approach to optimizing the development of oil fields. The objective function includes weighted squares of development target indicators and regularizing terms, in which the coefficients are searched adaptively. Regularizing terms ensure the fulfillment of restrictions on the optimized parameters and the rapid convergence of the optimization process. When minimizing the objective function, linearization of the target indicators is performed, and the values of the optimized parameters at the next iteration are sought by solving the system of linear algebraic equations obtained from minimizing the quadratic functional. The values of the target indicators and their sensitivity to the parameters being optimized are calculated by fluid dynamic 3D modeling for the oil reservoir model obtained as a result of history matching for the period preceding the optimization period. Calculations are performed in a distributed computing system consisting of multi-core personal computers. To test the proposed approach, a model of a high-viscosity oil field in Tatarstan was used. The optimization was carried out with various weighting factors and desired oil recovery values in the corresponding target indicator. It is shown that the optimized plans provide more efficient development of the oil field compared to the plan used in practice. At the same time, the optimal plan, built on the basis of a reservoir model history-matched at an early stage of development, optimizes development for a model history-matched throughout the entire period of field development. This allows us to conclude that development plans obtained from a model history-matched using a short time period will optimize production characteristics for a real field to about the same extent. The time for solving optimization problems containing about 500 parameters in a distributed computing system was about a day.

This work was supported by the Ministry of Science and Higher Education of the Russian Federation (project No. FSUN-2020-0012).
  1. Shirangi M. G., Durlofsky L. J. Closed-loop field development under uncertainty by use of optimization with sample validation. SPE Journal. Society of Petroleum Engineers, 2015, vol. 20, pp. 908–922. https://doi.org/10.2118/173219-PA
  2. de Brito D. U., Durlofsky L. J. Well control optimization using a two-step surrogate treatment. Journal of Petroleum Science and Engineering, 2020, vol. 187, art. 106565. https://doi.org/10.1016/j.petrol.2019.106565
  3. Bai Y., Hou J., Liu Y., Zhao D., Bing S., Xiao W., Zhao W. Energy-consumption calculation and optimization method of integrated system of injection-reservoir-production in high water-cut reservoir. Energy, 2022, vol. 239, art. 121961. https://doi.org/10.1016/j.energy.2021.121961
  4. Nasir Y., Yu W., Sepehrnoori K. Hybrid derivative-free technique and effective machine learning surrogate for nonlinear constrained well placement and production optimization. Journal of Petroleum Science and Engineering, 2020, vol. 186, art. 106726. https://doi.org/10.1016/j.petrol.2019.106726
  5. Negahdari Z., Khandoozi S., Ghaedi M., Malayeri M. R. Optimization of injection water composition during low salinity water flooding in carbonate rocks: A numerical simulation study. Journal of Petroleum Science and Engineering, 2022, vol. 209, art. 109847. https://doi.org/10.1016/j.petrol.2021.109847
  6. Tugan M. F., Weijermars R. Improved EUR prediction for multi-fractured hydrocarbon wells based on 3-segment DCA: Implications for production forecasting of parent and child wells. Journal of Petroleum Science and Engineering, 2020, vol. 187, art. 106692. https://doi.org/10.1016/j.petrol.2019.106692
  7. Alfarizi M. G., Stanko M., Bikmukhametov T. Well control optimization in waterflooding using genetic algorithm coupled with Artificial Neural Networks. Upstream Oil and Gas Technology, 2022, vol. 9, art. 100071. https://doi.org/10.1016/j.upstre.2022.100071
  8. Ng C. S. W., Ghahfarokhi A. J., Amar M. N. Production optimization under waterflooding with Long Short-Term Memory and metaheuristic algorithm. Petroleum, 2022, vol. 9, iss. 1, pp. 53–60. https://doi.org/10.1016/j.petlm.2021.12.008
  9. Tang L., Li J., Lu W., Lian P., Wang H., Jiang H., Wang F., Jia H. Well control optimization of waterflooding oilfield based on deep neural network. Geofluids, 2021, vol. 2021, art. 8873782. https://doi.org/10.1155/2021/8873782
  10. Jansen J. D., Douma S. D., Brouwer D. R., Van den Hof P. M. J., Bosgra O. H., Heemink A. W. Closed loop reservoir management. SPE Reservoir Simulation Symposium, The Woodlands, Texas, February 2009, art. SPE-119098-MS. https://doi.org/10.2118/119098-MS
  11. Wang C., Li G., Reynolds A. C. Production optimization in closed-loop reservoir management. SPE Journal, 2009, vol. 14, iss. 3, pp. 506–523. https://doi.org/10.2118/109805-PA
  12. Awasthi U., Marmier R., Grossmann I. E. Multiperiod optimization model for oilfield production planning: Bicriterion optimization and two-stage stochastic programming model. Optimization and Engineering, 2019, vol. 20, pp. 1227–1248. https://doi.org/10.1007/s11081-019-09455-0
  13. Rodriguez A. X., Aristizabal J., Cabrales S., Gomez J. M., Medaglia A. L. Optimal waterflooding management using an embedded predictive analytical model. Journal of Petroleum Science and Engineering, 2022, vol. 208, pt. B, art. 109419. https://doi.org/10.1016/j.petrol.2021.109419
  14. Dang C., Nghiem L., Nguyen N., Yang C., Chen Z., Bae W. Modeling and optimization of alkaline-surfactant-polymer flooding and hybrid enhanced oil recovery processes. Journal of Petroleum Science and Engineering, 2018, vol. 169, pp. 578–601. https://doi.org/10.1016/j.petrol.2018.06.017
  15. Zhao H., Xu L., Guo Z., Liu W., Zhang Q., Ning X., Li G., Shi L. A new and fast waterflooding optimization workflow based on INSIM-derived injection efficiency with a field application. Journal of Petroleum Science and Engineering, 2019, vol. 179, pp. 1186–1200. https://doi.org/10.1016/j.petrol.2019.04.025
  16. Xue X., Chen G., Zhang K., Zhang L., Zhao X., Song L., Wang M., Wang P. A divide-and-conquer optimization paradigm for waterflooding production optimization. Journal of Petroleum Science and Engineering, 2022, vol. 211, art. 110050. https://doi.org/10.1016/j.petrol.2021.110050
  17. Persova M. G., Soloveichik Y. G., Vagin D. V., Grif A. M., Kiselev D. S., Patrushev I. I., Nasybullin A. V., Ganiev B. G. The design of high-viscosity oil reservoir model based on the inverse problem solution. Journal of Petroleum Science and Engineering, 2021, vol. 199, art. 108245. https://doi.org/10.1016/j.petrol.2020.108245
  18. Soloveichik Y. G., Persova M. G., Grif A. M., Ovchinnikova A. S., Patrushev I. I., Vagin D. V., Kiselev D. S. A method of FE modeling multiphase compressible flow in hydrocarbon reservoirs. Computer Methods in Applied Mechanics and Engineering, 2022, vol. 390, art. 114468. https://doi.org/10.1016/j.cma.2021.114468
  19. Persova M. G., Soloveichik Y. G., Vagin D. V., Kiselev D. S., Sivenkova A. P., Grif A. M. Improving the computational efficiency of solving multisource 3-D airborne electromagnetic problems in complex geological media. Computational Geosciences, 2021, vol. 25, iss. 6, pp. 1957–1981. https://doi.org/10.1007/s10596-021-10095-6
  20. Nasybullin A. V., Persova M. G., Orekhov E. V., Shaidullin L. K., Soloveichik Y. G., Patrushev I. I. Modeling of surfactant-polymer flooding on Bureikinskoye field block. Neftyanoe khozyaystvo [Oil Industry], 2022, iss. 7, pp. 38–42 (in Russian). https://doi.org/10.24887/0028-2448-2022-7-38-42