Carbon dioxide removal (CDR) options - COFFEE-TEA
Note: The documentation of COFFEE-TEA is 'under review' and is not yet 'published'!| Corresponding documentation | |
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| Previous versions | |
| Model information | |
| Model link | |
| Institution | COPPE/UFRJ (Cenergia), Brazil, http://www.cenergialab.coppe.ufrj.br/. |
| Solution concept | General equilibrium (closed economy) |
| Solution method | The COFFEE model is solved through Linear Programming (LP). The TEA model is formulated as a mixed complementary problem (MCP) and is solved through Mathematical Programming System for General Equilibrium -- MPSGE within GAMS using the PATH solver. |
| Anticipation | |
Storage is the last step of Carbon Capture and Storage (CCS), and involves finding a location or a way to store carbon dioxide, which was previously captured (separated and purified) from carbon sources. Several studies address the importance of CCS in achieving GHG emission reduction, Nonetheless, there is not a lot of literature on detailed assessment on the amount of reservoir capacity for carbon storage. The methodology used in this study differentiates between Enhanced Oil Recovery (EOR), Gas Fields and Saline Aquifers. For EOR, the potential was calculated from the amount of oil available for EOR with carbon storage coefficient from the literature. This coefficient varied between 0.27 to 0.32 tCO2/bbl across regions, according to IEA-GHG (2009a).
As for gas fields, the procedure was basically the same as for EOR, but the gas fields were used. The coefficient for carbon storage was estimated from IEA-GHG (2009b), and varied from 2.3 to 2.9 tCO2/kNm³.
The last storage option, saline aquifers, has the highest storage potential, but there are many uncertainties regarding the extent to which the potential capacity can become usable storage (IEA-GHG, 2008). Another important stage of CCS, which can result in a considerable fraction of the cost associated with CO2 storage is the transportation stage. Depending on the distance and the type of terrain where the CO2 must pass, the costs associated with transportation can easily overcome the injection costs.
In order to estimate transportation costs, an analysis was made using a Geographic Information System (GIS) environment to estimate average distance between carbon storage reservoirs and main emissions sources. The location of major urban agglomerations was used as a proxy for emissions sources. As for the destination, the location of the main oil and gas fields around the world was used. Also, to simplify the manipulation of data inside the model and simplify calculation time within the model, four standard steps for distance were used for all regions: 100 km; 500 km; 1,000km; and over 2,000 km.