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Mixed-integer programming models for optimization of scheduling low salinity water injection during enhanced oil recovery in oil reservoirs

Author

Listed:
  • Zahra Mardani-Boldaji

    (Isfahan University of Technology)

  • Mohammad Reisi-Nafchi

    (Isfahan University of Technology)

  • Hamidreza Shahverdi

    (Isfahan University of Technology)

Abstract

Given the significance of oil in meeting the global energy demand, it is imperative to examine oil production methodologies, particularly Enhanced Oil Recovery (EOR). Enhanced Oil Recovery (EOR), by techniques such as fluid injection into the reservoir, establishes the requisite conditions for oil extraction. Low Salinity Water injection is a compelling alternative due to its availability and low injection costs. This study has concentrated on scheduling EOR operations with Low Salinity Water Injection (LSWI) to optimize the total profit. We present an innovative method for scheduling EOR operations after Breakthrough Time. This method considers both LSWI and reservoir conditions, allowing for selecting various water types with differing concentrations. In light of the non-linear characteristics of Cumulative Oil Production in oil reservoirs, we propose a Mixed-Integer Non-linear Programming model. Furthermore, we provide a novel approach utilizing a Mixed-Integer Linear Programming model. Validation through a hydrocarbon reservoir simulator verifies that the proposed models can effectively address the problem. Our findings, thus, indicate that in 11 of 20 problem instances, the selection of water type for injection considerably affects both response quality and profitability in EOR operations.

Suggested Citation

  • Zahra Mardani-Boldaji & Mohammad Reisi-Nafchi & Hamidreza Shahverdi, 2025. "Mixed-integer programming models for optimization of scheduling low salinity water injection during enhanced oil recovery in oil reservoirs," Operational Research, Springer, vol. 25(2), pages 1-32, June.
    • Handle: RePEc:spr:operea:v:25:y:2025:i:2:d:10.1007_s12351-025-00932-2
    DOI: 10.1007/s12351-025-00932-2
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    References listed on IDEAS

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    1. Neda Beheshti Asl & S. A. MirHassani & S. Relvas & F. Hooshmand, 2022. "A novel two-phase decomposition-based algorithm to solve MINLP pipeline scheduling problem," Operational Research, Springer, vol. 22(5), pages 4829-4863, November.
    2. Calderón, Andrés J. & Pekney, Natalie J., 2020. "Optimization of enhanced oil recovery operations in unconventional reservoirs," Applied Energy, Elsevier, vol. 258(C).
    3. Tapia, John Frederick D. & Lee, Jui-Yuan & Ooi, Raymond E.H. & Foo, Dominic C.Y. & Tan, Raymond R., 2016. "Optimal CO2 allocation and scheduling in enhanced oil recovery (EOR) operations," Applied Energy, Elsevier, vol. 184(C), pages 337-345.
    4. M. Taherkhani & M. Seifbarghy & R. Tavakkoli-Moghaddam & P. Fattahi, 2020. "Mixed-integer linear programming model for tree-like pipeline scheduling problem with intermediate due dates on demands," Operational Research, Springer, vol. 20(1), pages 399-425, March.
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