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- W4386475407 abstract "Effective management of waterflooding requires optimization of injection and production rates. However, nominal optimization methods based on a single realization can result in suboptimal hydrocarbon recovery and sweep efficiency due to reservoir heterogeneity. To account for geological uncertainties, this paper proposes a streamline-based robust optimization workflow that iteratively adjusts well rates of each realization using joint well pair rate multiplier calculated from an optimal interpolation method. This optimization process is applied over time intervals until the end of field life. The effective and robustness of this approach are validated using a 2D synthetic case and then applied to Brugge benchmark case. Total 100 realizations are created for each case, divided into training and testing ensembles. The entire training ensemble is used for robust optimization, while nominal optimization is conducted on each realization in the training ensemble. Two types of optimal schedules are applied to the testing ensemble for comparison and validation. Results indicate that the proposed streamline-based rate allocation optimization effectively adjusts well rates based on well-pair efficiency, while nominal optimized schedules produce worse results due to geological uncertainty. The robust optimized schedule improves cumulative oil production and reduces project risk. Overall, this approach provides an effective way to optimize waterflooding management under geological uncertainties." @default.
- W4386475407 created "2023-09-07" @default.
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- W4386475407 date "2023-12-01" @default.
- W4386475407 modified "2023-10-17" @default.
- W4386475407 title "Robust optimization under geological uncertainties using streamline-based rate allocation with data assimilation" @default.
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- W4386475407 doi "https://doi.org/10.1016/j.geoen.2023.212309" @default.
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