Reservoir simulation is widely used for field development planning in many fields and the evaluation of uncertainty range in production forecast is indispensable to make decision for further investment. Reservoir simulation model consists of geological, petrophysical and reservoir engineering parameters for each cell and cell boundary. These reservoir model parameters are usually defined based on limited available data in consideration of their uncertainty range. Therefore, the identification of influential parameters and the reduction of uncertainty range for these parameters are key components to mitigate the prediction uncertainty. An Upper Jurassic carbonate reservoir in Field A located in offshore Abu Dhabi has long production history for more than 30 years. Field A experienced several development schemes including natural depletion, crestal gas injection and crestal water injection. The current reservoir simulation model reasonably replicates historical performance on pressure, water cut evolution and GOR trend in field and well-by-well scales. On the other hand, we identified some reservoir model parameters have high uncertainty due to reservoir complexity and lack of reliable data. In this study, we focused on the identification of influential parameters on production forecast and the reduction of parameter uncertainty range using an experimental design approach. More than 200 simulation cases were generated with different combination of selected parameters using Latin Hypercube Sampling method. In each case, we evaluated history matching quality in field scale and relationship between history matching quality and each parameter. We found some parameters have correlation with history matching quality independently from the other parameters settings. This means that the uncertain range of those parameters can be reduced to achieve an acceptable history match irrespective of the other parameters. Furthermore, the prediction uncertain range was analyzed using the selected cases showing reasonable history matching quality to investigate the relationship between cumulative oil production and each parameter. The results indicated some parameters have a stronger impact on production forecast and their uncertainty range need to be reduced by further data gathering or considering other mitigation plans. This study successfully demonstrated that the proposed multiple parameter sensitivity analysis by effective use of experimental design approach enables to reduce the parameter uncertain range and identify the key influential parameters. Furthermore, this study result contributes to the prioritization and optimization of future data gathering plan in Field A.