Abstract

Optimizations of CO2 Water Alternating Gas(WAG)- systems with multi-objectives of incremental recovery and maximization of CO2 storage are challenging. The incorporation of a total Greenhouse gas (GHG) life cycle assessment is mostly ignored leading to inaccurate estimation of overall net carbon emissions of their operations. In this study, the effect of a total GHG life cycle assessment on a multi-objective CO2-WAG optimization with integrated techno-economic assessment (TEA) which factors carbon tax credit is conducted. A life cycle assessment (LCA) was conducted utilizing a 20 -year optimized post history matched data from a high fidelity reservoir simulation model. Using data generated from the optimum result, a techno-economic life cycle analysis was further conducted. The first scenario classified as the base model had an estimated 81% of purchased CO2 sequestered. The results through a comprehensive techno-economic LCA model yielded a net estimate of 73% of purchased CO2. The optimized forecasted model which considered key operational and reservoir factors such as WAG ratio, injection rates and periods, and well specification resulted in an improved sequestration of 92% of purchased CO2. However, this also dropped to 84% after taking it through LCA. These results clearly indicate a significant amount of net CO2 is not accounted for when operations are not analyzed through LCA. From the LCA, direct flaring volumes of CO2, energy consumption and efficiency of unit equipment were noticed to be the major causes of these reductions. Considering ten main sources of energy as source of energy generation, a comparative techno-eco LCA was conducted. The results confirmed a lower net volume and NPV for energy sources with higher carbon footprints and vice versa. Thus, a total LCA of CO2-WAG greatly influences net storage factor of purchased CO2 and hence project NPV where tax credit/incentives per ton of CO2 sequestered is considered. Although operational conditions are optimized for best results, there are significant factors that leads to minimization of net storage factor. This study therefore provides an insightful information for optimizing CO2-WAG multi-objectives to achieve minimum GHG emission.

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