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

• Published in: Operational research Vol. 25; no. 2; p. 48
• Main Authors: Mardani-Boldaji, Zahra, Reisi-Nafchi, Mohammad, Shahverdi, Hamidreza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025; Springer Nature B.V
• Subjects: Business and Management; Computational Intelligence; Enhanced oil recovery; Fluid injection; Integer programming; Linear programming; Management Science; Mixed integer; Nonlinear programming; Operations Research; Operations Research/Decision Theory; Optimization; Original Paper; Reservoirs; Salinity; Scheduling; Water flooding; Water injection; Cumulative oil production; Scheduling in enhanced oil recovery operations; Mixed-integer programming; Low salinity water injection
• ISSN: 1109-2858; 1866-1505
• DOI: 10.1007/s12351-025-00932-2

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.