Capacitance-Resistive Model (CRM), as a fast yet efficient proxy model, suffers from some limitations in modeling relatively complex reservoirs. Some current improvements on this proxy made it a more powerful simulator with updating parameters over time. However, the model’s intrinsic uncertainty arisen from simplifying fluid-flow modeling by some limited number of constant parameters is not addressed yet. In this study, this structural limitation of CRM has been addressed by introducing a time-correlated model error, including stochastic and non-stochastic parameters, embedded into this proxy’s formulation. The error term’s non-stochastic parameters have been tuned to be used in forecasting reservoir performance while applying the stochastic parameter leads to generating a range of possible results. The number of stochastic runs is obtained in a direct relationship with the discrepancy in history matching problem. By averaging the obtained possible results, the intended profile of the model output is achieved. To validate the developed approach, data from an Iranian layered oil reservoir has been applied to simulate the trend of CRM results’ discrepancy from true data by using the time-correlated model error. The model’s obtained results have been compared to the available field data and show that by accounting for the error-related parameters, the values of the average model discrepancy from historical data and r-squared are obtained effectively equal to 13.1 bbl/D and 0.91, respectively. These parameters are respectively 41.6 bbl/D and 0.72 in case of not considering the model error. Additionally, oil production rates have been calculated by employing a fractional-flow model and compared with Production Logging Tools (PLT) data from the field that depicts the model’s satisfying performance

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