Abstract

CO2 geological storage in depleted oil and gas reservoirs is recognized as a vital approach to combat anthropogenic CO2 emissions. However, the associated challenges of high operational costs and potential CO2 leakage necessitate the development of a cost-effective and environmentally sustainable monitoring program. This study presents a multi-objective optimization model aimed at designing an efficient CO2 monitoring program that meets both economic and environmental requirements. The model is applied to a case study of a Carbon Capture and Storage project in Shaanxi, utilizing an enhanced strengthen elitist genetic algorithm to solve the model under various scenarios. The analysis results demonstrate that in any scenario, a 10 % increase in the required monitoring technology precision leads to an average 1 % rise in monitoring costs, highlighting the cost-effectiveness of the CO2 monitoring scheme developed using our model. Furthermore, when environmental targets are raised by 10 times, monitoring costs show an average increase of 0.92 times, further illustrating the environmentally sustainable nature of our proposed CO2 monitoring scheme. In conclusion, this study provides a cost-effective and environmentally sustainable method framework for the development of CO2 geologic sequestration monitoring scheme, which can contribute to the practical implementation of Carbon Capture and Storage projects to a certain extent.

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