Seismic interpretation is an important step in the reservoir model building workflow, and involves mapping the main surfaces and faults to establish the structural framework of the studied reservoir. However, uncertainties are inherently related to the seismic data due to several factors, such as limited registered bandwidth, structural and stratigraphic complexities associated to the reservoir encasing rocks and overburden, to seismic acquisition and processing workflows, energy spreading, tuning effects and noise, among others. Another important source of uncertainty – which is seldom treated – is conceptual uncertainty introduced by the interpreter. Bond et al. (2007), for example, present different interpretations obtained from the same seismic image. Despite the nature of the sources, these effects must be addressed in order to predict their impact on subsequent reservoir modelling steps and volume calculations. MacDonald et al. (2009) provide a description of the uncertainties present in each step of the reservoir modelling process. Leahy and Skorstad (2013) present a new workflow to quantify the uncertainties in seismic interpretation and use this information in the further modelling steps. Leahy et al. (2014) use the horizon uncertainty information to enhance the quality of the surface mapping, and hence reduce ambiguity on the interpreted surface. However, irrespective of the advantages in correctly identifying and quantifying the level of uncertainty on the interpretation of geological surfaces, it remains a highly labour intensive and time-consuming activity. Many interpreters do not have the available time or expertise to perform a detailed inspection of their data. Furthermore, each interpreter may quantify uncertainty in subtly different ways, creating some difficulty when comparing reservoir interpretations. The result is that all too often uncertainty analysis is superficially performed or completely absent, impacting the economic evaluation of the prospects. In order to provide valuable uncertainty analysis as fast and as accurately as possible, a new methodology to quickly assess the uncertainty information based on seismic data has been developed. This methodology lets the interpreter construct an uncertainty map through a combination of seismic attributes and can be used as initial, or ‘a priori’, information to correctly discriminate the low and high-resolution regions that correspond to areas of major and minor uncertainties, respectively. This approach of building the uncertainty map and the details of each step are described in the next section.