Iranian Chemical Engineering Journal

Iranian Chemical Engineering Journal

Optimization of EOR Well Constraints Using Surrogate Modelsand NSGA II: Low-Salinity Water Flooding

Document Type : Original Article

Authors
1 MSc. Student of Petroleum Engineering, Tabriz University of Technology
2 Professor of Petroleum Engineering, Tabriz University of Technology
Abstract
A significant portion of the world's oil reserves is hosted in carbonate formations. Recent studies on low-salinity water injection have shown that reducing the salinity of the injected water can significantly enhance oil recovery. However, optimizing well operating conditions during low-salinity water injection remains a major challenge due to the process complexity and the computational cost of reservoir simulations.
Unlike previous works, this study employed a combination of a machine-learning-based surrogate model and a multi-objective genetic algorithm to simultaneously model and optimize well operating constraints, including maximum oil production rate, minimum bottom-hole pressure, water injection rate, and perforation status (open/closed). The developed surrogate model demonstrated high accuracy (R² = 0.989 for training and 0.984 for testing) and significantly reduced the simulation time. Subsequently, by considering net present value, oil recovery factor, and water cut as objective functions, a set of optimal solutions was obtained on the Pareto front. The results indicated that optimal well conditions could lead to a $135 million increase in net present value and an oil recovery factor of up to 65.54%.
Keywords
Subjects

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