Brown-field Experimental Design (ED) was successfully applied to a super-giant oilfield to generate probabilistic (P10, P50, and P90) models to define the range of field performance and to mitigate the non-uniqueness in reservoir simulation. A recent trend in reservoir simulation has been to apply probabilistic modeling, such as, brown-field ED to develop multiple (P10, P50, and P90) models. Unfortunately, these probabilistic models are also non-unique because multiple input combinations can be used to generate the probabilistic responses observed during ED. The non-uniqueness of the probabilistic models may impact their usefulness in certain circumstances. For example, if these models are used to develop short-term signposts for long-term reservoir behavior, then the models may be influenced by the selection of reservoir data (e.g., a P10 model with one combination of input may have a different short-term "signature" than an alternate P10 model despite giving comparable P10 recovery). Also, the degree of success of a downside-mitigation (or upside-capture) strategy, and its ranking with other such strategies may be influenced by the input chosen to develop the models. For the super-giant Tengiz oilfield, brown-field ED was applied to a conventional history match with the primary objective of creating probabilistic models. Additionally, we developed tools to design multiple deterministic models with specific physical interpretations. With these deterministic models we can identify the signatures for specific reservoir phenomenon, such as, minimum/maximum OOIP, minimum/maximum compartmentalization, minimum/maximum reservoir energy, etc. All models built with these tools yield acceptable visual and quantitative history matches. In this paper we discuss how brown-field ED was used to post-process a conventional history match. We present a case study for the use of brown-field ED methods and illustrate the proposed approach to mitigate the non-unique nature of reservoir simulation. While the impact non-uniqueness can be mitigated, we also recognized that it can never be completely eliminated.