MIICE - Model of Iterative Investment in CCS with CO2-EOR

MIICE, model of iterative investment in carbon capture and storage (CCS) with CO₂ enhanced oil recovery (EOR), is developed to assess the techno-economic conditions under which CO₂-EOR could catalyze the gigatonne-scale deployment of CCS or not. MIICE takes a profit maximizing approach to find the conditions under which risk averse investors would develop CCS with the revenue from producing oil via EOR. Input variables include the capital cost of CO₂ capture, a range of tax on CO₂, oil price, technological learning and oil price growth rate. All input variables and constants considered are easily adjustable in the model.

MIICE uses randomized variables to generate the characteristics of 10,000 hypothetical oil fields for EOR and considers only 1,000 of these as available. Their characteristics are based on characteristic data from existing oil fields, and each combination of characteristics defines a unique field. For each CCS with EOR project, the model establishes a field development strategy to minimize EOR costs and maximizes revenue. MIICE will choose from the option of capture plant sizes of 1-4 MtCO₂ captured/year and will only select the size that has highest NPV when coupled with one of the 1,000 field options. Financial modelling assumptions include discount rate, inflation rate, tax scheme on oil production, industrial growth rate limitations and more, all of which can be easily manipulated in the model. 

MIICE was originally developed in MATLAB and the randomization seed used will influence the number of successful projects developed (the sensitivity to this is explored and presented in the paper). MIICE is also available here in GNU Octave, a free software compatible with MATLAB. However, because of the nature of the randomization seed, the final results are slightly different since the oil field characteristics deemed available will differ slightly with seed value. Nevertheless, the overall results of the analysis and conclusions remain the same regardless of the seed. 

The authors advise that the current version of the model be run in MATLAB as the run time is substantially shorter than when run in GNU Octave.

If you use MIICE please cite: http://pubs.rsc.org/en/content/articlehtml/2017/ee/c7ee02102j 

For any questions please contact Clea Kolster at clea.kolster10@imperial.ac.uk or any of the co-authors mentioned above.