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Published: 2007
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The primary goal of this project was to demonstrate a new and novel approach for high resolution, 3D reservoir characterization that can enable better management of CO2 enhanced oil recovery (EOR) projects and, looking to the future, carbon sequestration projects. The approach adopted has been the subject of previous research by the DOE and others, and relies primarily upon data-mining and advanced pattern recognition approaches. This approach honors all reservoir characterization data collected, but accepts that our understanding of how these measurements relate to the information of most interest, such as how porosity and permeability vary over a reservoir volume, is imperfect. Ideally the data needed for such an approach includes surface seismic to provide the greatest amount of data over the entire reservoir volume of interest, crosswell seismic to fill the resolution gap between surface seismic and wellbore-scale measurements, geophysical well logs to provide the vertical resolution sought, and core data to provide the tie to the information of most interest. These data are combined via a series of one or more relational models to enable, in its most successful application, the prediction of porosity and permeability on a vertical resolution similar to logs at each surface seismic trace location. In this project, the procedure was applied to the giant (and highly complex) SACROC unit of the Permian basin in West Texas, one of the world's largest CO2-EOR projects and a potentially world-class geologic sequestration site. Due to operational scheduling considerations on the part of the operator of the field, the crosswell data was not obtained during the period of project performance (it is currently being collected however as part of another DOE project). This compromised the utility of the surface seismic data for the project due to the resolution gap between it and the geophysical well logs. An alternative approach was adopted that utilized a relational model to predict porosity and permeability profiles from well logs at each well location, and a 3D geostatistical variogram to generate the reservoir characterization over the reservoir volume of interest. A reservoir simulation model was built based upon this characterization and history-matched without making significant changes to it, thus validating the procedure. While not the same procedure as originally planned, the procedure ultimately employed proved successful and demonstrated that the general concepts proposed (i.e., data mining and advanced pattern recognition methods) have the flexibility to achieve the reservoir characterization objectives sought even with imperfect or incomplete data.
