Changes

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.

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, 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.

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 [http://www.merriam-webster.com/dictionary/outcrop 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==