In this paper we demonstrate how key geological uncertainties in a giant onshore carbonate reservoir in the Middle East, most notably fracture permeability and saturation distributions, impact the quality of the history match and change the performance forecasts of a planned miscible water alternating gas (MWAG) injection process.

Different geological models for the reservoir were designed by integrating static and dynamic data. These data indicated the need to consider fault‐related fractures using effective medium theory (EMT) and to update the saturation distribution by integrating special core analysis and log‐derived J‐functions in the reservoir model during the history matching. Afterward, multiobjective optimization (MOO) was applied for each history‐matched model to identify well controls that optimally balanced the need to maximize the time on the plateau rate while adhering to the field's gas production constraints.

Our results clearly show that including low‐intensity fault‐controlled fractures in the reservoir model improved the quality of the history match for the gas/oil ratio (GOR), BHP and water cut (WC). This is especially true for wells located near faults, which were difficult to match in the past. Moreover, our results further show that the updated saturation model improved the quality of the history match for the WC, and honored water saturation from the log with high accuracy, particularly for wells located in the transition zone.

Applying MOO for each history‐matched model then allowed us to identify well controls for the MWAG injection that could extend the time at which the reservoir would be produced at the plateau rate for up to 11 years and the risk of losing production plateau down to 2 years, while always adhering to the current field operational constraints.

We demonstrate how the integration of MOO with an innovative workflow for fracture and saturation modeling impacts the prediction of a planned MWAG injection in a giant onshore carbonate reservoir. Our work clearly illustrates the potential of integrating MOO with new reservoir characterization methods to improve the quantification of uncertainties in reservoir performance predictions in carbonate reservoirs.

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