Summary This work presents a qualitative analysis of the uncertainties in performance prediction for a reservoir characterization process in which static and dynamic data are combined. Using primary, singlephase production data as the dynamic constraint in the inversion, the quality of the models thereby obtained is evaluated when they are used in alternative or subsequent production scenarios. The conclusions are that, provided the well configuration remains the same as that used for the dynamic constraint in the inversion, the models perform well when the production stage remains single- phase, primary depletion, even at the individual well level and also for future prediction. For multi-phase primary production, there is also a reasonable match in performance, although not as good as for a single-phase case. This is because such depletion processes are controlled by the permeability in the near-wellbore region, which are adequately reconstituted by the characterization process used, while the multi-phase density variations with time were observed to have little impact. However, the fine scale, inter-well permeability patterns of the different realizations obtained by the integration of the dynamic and static data are not sufficiently resolved and so are unable to adequately capture the interwell permeability heterogeneity patterns which are necessary for accurate waterflood performance matching. Introduction It is important and becoming more popular to integrate the static and dynamic data for reservoir characterization. By integrating different types of data, the final reservoir characterization will be more precise or unique. In other words, the uncertainty of the reservoir description will be reduced. However, for optimal reservoir management, it is more critical to understand the uncertainty of the future performance prediction. In geostatistics, the norm is to generate multiple realizations of the reservoir description and conduct the reservoir simulation. The multiple realizations of the future performance prediction form the prediction error bound. This is a more flexible approach than the traditional serial modeling approach for performance prediction. Conventionally, the geostatistical modeling incorporates the static data -- such as seismic, geological data -- quite well, but may not reproduce the dynamic performance. The incorporation of the dynamic data into the modeling may yield better history matching. However, the prediction characteristics need to be understood. Recently, inverse theory and other approaches have been applied to integrate the static and dynamic data. In 1992, Deutsch applied simulated annealing to integrate the engineering data with variogram data for geostatistical modeling. In 1994, Sagar et al. applied simulated annealing to incorporate well test data and the spatial relationship for modeling the permeability field with more efficient forward modeling. In 1994, Oliver applied inversion methods to incorporate the spatial relationship and well-test data to generate multiple realizations of the permeability field. Chu et al. implemented a sensitivity coefficient approach in a more efficient way to perform the inversion. In 1995, Reynolds et al. extended that work and achieved better efficiency by using reparameterization. All these methods use the spatial relationship and dynamic data as constraints for the inversion of the permeability field. This works well if the spatial relationship is available and the correlation range is long. However, if the spatial relationship is not easy to obtain, this approach may not work. Also, honoring the spatial relationship does not necessarily mean honoring other data such as geological description or seismic modeling results. Recently, Huang and Kelkar proposed a method to integrate the dynamic data with static data -- especially seismic data -- which obviates the need to honor the spatial relationship such as variograms.