Azevedo, Leonardo (CERENA/IST) | Amaro, Catarina (CERENA/IST) | Grana, Dario (Department of Geology and Geophysics, University of Wyoming) | Soares, Amílcar (CERENA/IST) | Guerreiro, Luís (Partex Oil & Gas)
Data integration is a key step in reservoir modeling and characterization. If done properly, the integration of different sources of information about the subsurface petro-elastic properties of interest allow a better description of the reservoir while accounting for existing uncertainties. Under this scope, geostatistical seismic inversion techniques have been successfully applied to integrate seismic reflection and well data for seismic reservoir characterization. Depending on the available seismic reflection data, these geostatistical modeling techniques allow inferring acoustic and/or elastic spatial distributions of the the subsurface. The resulting elastic models are then used as secondary variables to infer the petrophysical properties (e.g. facies, porosity) of the reservoir. Consequently, these petrophysical models are not directly constrained by the existing seismic reflection data and the uncertainties related with the seismic inversion problem are not properly propagate during the entire geo-modeling procedure. This work introduces a framework to incorporate rock physics modeling within conventional pre-stack iterative geostatistical seismic inversion methodologies. The proposed technique allow inverting from pre-stack seismic reflection data directly for facies, porosity and pore fluid. This technique is based on three main concepts: i) the model parameter space is perturbed using stochastic sequential simulation and co-simulation; ii) a statistical rock physics modeling technique is used to link the elastic and petrophysical domain within the inversion procedure; and iii) the use of a global optimizer based on cross-over genetic algorithms driven by the mismatch between real and synthetic seismic reflection from iteration to iteration. This work illustrates the successful application of the proposed iterative geostatistical seismic inversion technique to a real dataset. The retrieved petro-elastic models are simultaneously, and consistently, conditioned by the well-log data, seismic reflection data and the reservoir geology (i.e. pore geometry) as expressed by a rock physics model.