Exploration and Production (E&P) business faces uncertainty of our knowledge about the Earth at all stages of the aggregated workflow. Every stage uses ambiguous input and generates ambiguous output that becomes input to the next stage. Thus, understanding, quantifying and tracking uncertainty across stages and disciplines are essential for the ultimate uncertainty that impacts decision-making. In particular, the structural uncertainty is derived from the model uncertainty as a result of seismic depth imaging. Whilst the underlying ambiguity can never be fully eradicated, a quantified measure of these uncertainties provides deeper understanding of the risks and leads to more informed decision making. In the past decade, with the advancement of sensor and computer technology, the seismic industry has made great improvements in data-acquisition designs as well as depth-imaging and model-building algorithms. However, the cost/benefit justification for the value of information (VoI) obtained by utilization of these technologies remains mainly qualitative. The same limitation applies to geoscience knowledge. This paper discusses how uncertainty analysis can lead to quantifying VoI and also how application of probabilistic assessment of our geoscience knowledge can lead to investment decision-making based on probabilistic appraisal. We demonstrate that uncertainty is a good metric for the value of knowledge and serves also as a unifying language for translating, transmitting, and enhancing knowledge as one E&P stage transitions to another and as one discipline communicates with another. The paper discusses a stochastic technique of this translation through evaluation of different appraisal parameters (gross rock volume, original oil in place, net primary production) as an extended application of seismic-based imaging and structural uncertainty derived from velocity model building.