Changes

==Geological input to site characterization==

==Geological input to site characterization==

The ideal characterization of geological storage sites for CO₂ requires a thorough integration of all geoscientific data. Data types change depending on the stage of characterization. Regional assessment requires low-resolution, long-range data sets, such as [[two-dimensional (2-D) seismic and stratigraphic drill holes. However, site-specific assessment requires more detailed data such as high-density 2-D or [[Three-dimensional seismic method|3-D seismic]], [[Overview of routine core analysis|core]], and many wells and logs. These different types of data are commonly available from petroleum exploration and production.

The ideal characterization of geological storage sites for CO₂ requires a thorough integration of all geoscientific data. Data types change depending on the stage of characterization. Regional assessment requires low-resolution, long-range data sets, such as two-dimensional (2-D) [[seismic data]] and stratigraphic drill holes. However, site-specific assessment requires more detailed data such as high-density 2-D or [[Three-dimensional seismic method|3-D seismic]], [[Overview of routine core analysis|core]], and many wells and logs. These different types of data are commonly available from petroleum exploration and production.

The regional data sets should be reviewed to evaluate the structural configuration and regional distribution of lithofacies. This is best done using a large 2-D seismic data set complemented with available well-log control and petrophysical data. The aim during this phase is to identify reservoir units with appropriate storage properties (such as adequate porosity for storage of substantial volumes of CO₂ and permeability for injectivity and subsequent dissemination into the pore system) overlain by suitably extensive and thick seals.

The regional data sets should be reviewed to evaluate the structural configuration and regional distribution of [[lithofacies]]. This is best done using a large 2-D seismic data set complemented with available well-log control and petrophysical data. The aim during this phase is to identify reservoir units with appropriate storage properties (such as adequate [[porosity]] for storage of substantial volumes of CO₂ and [[permeability]] for injectivity and subsequent dissemination into the pore system) overlain by suitably extensive and thick seals.

The high-density data sets are needed to evaluate the reservoir and seal geometries and architectures of the identified storage site in more detail. The aim of a full data set integration and interpretation is to assess the migration pathway of injected CO₂ as well as to assess the potential storage volume. This is best done by building a detailed static 3-D [[reservoir model]], which can be upscaled for dynamic fluid flow simulations. Various iterations of the dynamic simulations should be incorporated by an integrated team to better understand the geological effects on CO₂ injection, migration, and storage.

The high-density data sets are needed to evaluate the [[reservoir]] and [[Seal rock|seal]] geometries and architectures of the identified storage site in more detail. The aim of a full data set integration and interpretation is to assess the [[migration]] pathway of injected CO₂ as well as to assess the potential storage volume. This is best done by building a detailed static 3-D [[Reservoir modeling for simulation purposes|reservoir model]], which can be upscaled for dynamic [[Fluid flow fundamentals|fluid flow]] simulations. Various iterations of the dynamic simulations should be incorporated by an integrated team to better understand the geological effects on CO₂ injection, migration, and storage.

Data challenges are frequently encountered when trying to assess geological storage sites for CO₂ because either the basin under assessment has not been explored by the petroleum industry or, more typically, the site under investigation lies just off-structure and thus commonly outside the major structurally controlled hydrocarbon fields with their dense data sets. In addition, data challenges are also common because of incomplete data sets, data loss, or simple data deterioration with time. Two types of solutions can be considered to overcome the data challenges. The best but most costly solution is data acquisition. Paying several millions of dollars for drilling a well is common, whereas the acquisition and processing of seismic data are equally expensive. A far more cost-effective but also less accurate method of overcoming data challenges is to use [[outcrop]] and [[subsurface]] analog data sets to model the subsurface geology at the storage site. Analog data sets are useful in that they provide generic quantitative data of a range of parameters paramount to a specific geological setting. For example, analogs can be used to predict sand body and shale geometries, connectivities, and heterogeneities. They can also be used for providing ranges and distributions of porosities and permeabilities and for providing estimates on likely seal capacities. Analog data sets to characterize geological storage sites for CO₂ are currently the most affordable and accessible data sets for reservoir characterization.

Data challenges are frequently encountered when trying to assess geological storage sites for CO₂ because either the basin under assessment has not been explored by the petroleum industry or, more typically, the site under investigation lies just off-structure and thus commonly outside the major structurally controlled hydrocarbon fields with their dense data sets. In addition, data challenges are also common because of incomplete data sets, data loss, or simple data deterioration with time. Two types of solutions can be considered to overcome the data challenges. The best but most costly solution is data acquisition. Paying several millions of dollars for drilling a well is common, and the acquisition and processing of seismic data are equally expensive. A far more cost-effective but also less accurate method of overcoming data challenges is to use [[outcrop]] and subsurface analog data sets to model the subsurface geology at the storage site. Analog data sets are useful in that they provide generic quantitative data of a range of parameters paramount to a specific geological setting. For example, analogs can be used to predict sand body and shale geometries, [[Connectivity_and_pore_throat_size#Connectivity|connectivities]], and [[Geological heterogeneities|heterogeneities]]. They can also be used for providing ranges and distributions of porosities and permeabilities and for providing estimates on likely [[Seal capacity|seal capacities]]. Analog data sets to characterize geological storage sites for CO₂ are currently the most affordable and accessible data sets for reservoir characterization.

==Monitoring==

==Monitoring==