Many oil reservoirs worldwide have cycle dependent oil recovery either by design (e.g. WAG injection) or unintended (e.g. repeated expansion/shrinkage of gas cap). However, to reliably predict oil recovery involving three-phase flow process, a transformational shift in the procedure to model such complex recovery method is needed. Therefore, this study focused on identifying the shortcomings of the current reservoir simulators to improve the simulation formulation of the cycle-dependent three-phase relative- permeability hysteresis.

To achieve this objective, several core-scale water-alternating-gas (WAG) injection experiments were analysed to identify the trends and behaviours of oil recovery by the different WAG cycles. Furthermore, these experiments were simulated to identify the limitations of the current commercial simulators available in the industry. Based on the simulation efforts to match the observed experimental results, a new methodology to improve the modelling process of WAG injection using the current simulation capabilities was suggested. Then the WAG injection core-flood experiments utilized in this study were simulated to validate the new approach.

The results of unsteady-state WAG injection experiments performed at different conditions were used in this simulation study. The simulation of the WAG injection experiments confirmed the positive impact of updating the three-phase relative-permeability hysteresis parameters in the later WAG injection cycles. This change significantly improved the match between simulation and WAG experimental results. Therefore, a systematic workflow for acquiring and analyzing the relevant data to generate the input parameters required for WAG injection simulation is presented. In addition, a logical procedure is suggested to update the simulation model after the third injection cycle as a workaround to overcome the limitation in the current commercial simulators.

This guideline can be incorporated in the numerical simulators to improve the accuracy of oil recovery prediction by any cycle-dependent three-phase process using the current simulation capabilities.

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