High demand for hydrocarbons incentivizes the industry to look for new exploration opportunities in unexplored areas with high risk where new potential discoveries of importance might be located. Frontier locations are unexplored or underexplored basins, in which geological and geophysical information might be unavailable, or sparse. In this paper we present a new methodology based on the Global Stochastic Inversion (GSI) algorithm (Soares et al., 2007; Caetano, 2009), which uses Direct Sequential Simulation (Soares, 2001) as a global perturbation method to generate equi-probable models of acoustic impedance, and follows a iterative process to optimize a previously defined objective function. The convergence of the inverse process is evaluated by the local and global correlation coefficient between real seismic and synthetic seismogram. This new method can be useful for preliminary assessment of different scenarios in unexplored areas, where no well log information exists to be used as a constrain to an inverse problem. The method follows the GSI workflow, but without using any well data in the study area. Instead of that, our proposal is to use analogs information (outcrops, modern analogs, and wells logs data from nearby fields) to condition the generation of acoustic impedance models. In this procedure, the geometry and position of the “analog-well” is ignored and the analog information is only used in the form of spatial dispersion and spatial patterns of acoustic impedances (histograms and variograms) for each lithology/facies expected in the geological model, as defined by an expert.


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