Accelerated optimization of CO2-miscible water-alternating-gas injection in carbonate reservoirs using production data-based parameterization

Enhancing oil recovery in reservoirs with light oil and high gas content relies on optimizing the miscible water alternating gas (WAG) injection profile. However, this can be costly and time-consuming due to computationally demanding compositional simulation models and numerous other well control variables. This study introduces WAG(eq), a novel approach that expedites the convergence of the optimization algorithm for miscible water alternating gas (WAG) injection in carbonate reservoirs. The WAG(eq) leverages production data to create flexible solutions that maximize the net present value (NPV) of the field, while providing practical implementation of individual WAG profiles for each injector. The WAG(eq) utilizes an injection priority index to rank the wells and determine which should inject water or gas at each time interval. The index is built using a parametric equation that considers factors such as producer and injector relationship, water cut (W-CUT), gas-oil ratio (GOR), and wells cumulative gas production, to induce desirable effects on production and WAG profile. To evaluate WAG(eq)'s effectiveness, two other approaches were compared: a benchmark solution named WAG(bm), in which the injected fluid is optimized for each well over time, and a traditional baseline strategy with fixed 6-month WAG cycles. The procedures were applied to a synthetic simulation case (SEC1_2022) with characteristics of a Brazilian pre-salt carbonate field with karstic formations and high CO2 content. The WAG(eq) outperformed the baseline procedure, improving the NPV by 6.7% or 511 USD million. Moreover, WAG(eq) required fewer simulations (less than 350) than WAG(bm) (up to 2000), while delivering a slightly higher NPV. The terms of the equation were also found to be essential for producing a WAG profile with regular patterns on each injector, resulting in a more practical solution. In conclusion, WAG(eq) significantly reduces computational requirements while creating consistent patterns across injectors, which are crucial factors to consider when planning a practical WAG strategy.