Seismic reservoir characterization aims to provide an accurate reservoir description of rock and fluid properties estimated from seismic data. However, in several applications, seismic data only, cannot accurately discriminate the fluid effect, and the integration of other geophysical measurements, such as electromagnetic data, is required to improve the reservoir description. In this work, we propose a joint rock physics inversion to estimate porosity and fluid saturations from seismic velocity and electrical resistivity. The method is based on a Bayesian approach to inverse modeling and combines inverse theory and statistical rock physics relations. The advantages of this approach are the joint estimation of rock properties, achieved by a coupled rock physics model, and the estimation of the uncertainty associated to the predicted model, achieved through the Bayesian approach. The method has been applied to a real dataset, the Rock Spring Uplift field in Wyoming, a CO2 sequestration study.
The goal of seismic reservoir characterization is to provide a reliable model of the reservoir, in terms of rock properties, such as porosity and lithology, and fluid saturations. In rock physics models, when rock properties are known, we can predict the effect of fluid saturations on P-wave and S-wave velocity and density (Mavko et al., 2009; and Dvorkin et al., 2014). However, the solution of the inverse problem, i.e. the estimation of rock and fluid properties from velocities and density, is generally a challenging task (Avseth et al., 2005; and Doyen, 2007). Indeed, the solution of the inverse problem is not necessarily unique: two different rocks could have different porosities, lithologies and fluids, and the same elastic response. Furthermore, when the inverse problem is solved using seismic data instead of well log data, the low resolution and low signal-to-noise ratio of the data often increase the uncertainty in the estimation of seismic velocities and density, which makes the rock-fluid property estimation more challenging. To improve the reservoir description and reduce the associated uncertainty, we propose to integrate electromagnetic (EM) data, together with seismic attributes, in the reservoir modeling workflow (Du and MacGregor, 2010; MacGregor, 2012).