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Waveform-based inversions have been receiving a considerable attention over the recent years in the oil and gas industry. Going beyond the assumptions behind the amplitude-variation-with-offset/angle inversion and honoring complex effects of wave propagation, such waveform-based methods are effective in accurately delineating the subsurface reservoir properties. In this work, we develop a prestack waveform inversion method using multilevel parallelization and apply it on a real data volume from the Rock-Springs uplift, Wyoming, USA. We further use the inversion results to identify some key formations. Additionally, because the primary purpose of acquiring the Rock-Springs uplift seismic data was to characterize the subsurface for carbon dioxide sequestration, we also use our inversion results to analyze some potential target reservoirs and their associated seals. By demonstrating that our analysis is capable of producing a high-resolution image of the subsurface elastic earth properties, we conclude that prestack waveform inversion is an effective tool for reservoir characterization.
Presentation Date: Tuesday, October 18, 2016
Start Time: 10:45:00 AM
Presentation Type: ORAL
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).