| dc.description.abstract | It is generally accepted that underground geological storage of CO2 (GSC) in deep aquifers, is the option with the highest potential storage capacity. Detailed subsurface models with a high level of subsurface understanding are required for both estimation of CO2 storage capacity and GSC site characterisation. To attain this high level of subsurface understanding, models need extensive well data and good 3D seismic coverage, often not available at potential GSC sites. The technology of Stratigraphic Forward Modelling (SFM) has advanced to the stage where numerical simulation of the depositional processes can be used to predict reservoir properties at appropriate scales, away from wells and below seismic resolution. With these predictive qualities, SFM could potentially provide the subsurface understanding required in GSC, even in locations with limited well data and no seismics. This study assesses and demonstrates the potential role of SFM in rapidly generating a well-constrained static reservoir model for generic use in the GSC workflow. The eastern Australian Surat Basin is considered a highly prospective basin for GSC with several potential GSC sites. CSIRO' s Sedsim SFM package has been used to create a stratigraphic forward model of the Surat Basin, including a high resolution nested model of the EPQ-7 CO2 sequestration tenement located in the basin. The model simulates deposition and burial of the Early Jurassic Precipice Sandstone, Early to Middle Jurassic Evergreen Formation and Middle Jurassic Hutton Sandstone. A SFM workflow is devised, in which model results are translated to (pseudo)gamma ray values, to be directly compared to gamma ray well logs. This workflow enables an efficient model-to-well tuning process. The model is based on literature describing the depositional processes in the basin, followed by a process of tuning the model to limited well data. In the basin centre and EPQ-7 tenement, the model results display a good match with well data. The model-generated porosity and permeability values are reasonable, yet could use further refinement. Several potential applications in further GSC workflows are proposed for the model. Finally, it is concluded that this study demonstrates the high potential value of SFM, using Sedsim, in rapidly generating static reservoir models for use in GSC workflows, especially in areas with limited well data. | |
