Abstract

Geological storage of CO2 in saline aquifers, depleted oil and gas reservoirs, and through enhanced oil recovery (EOR) is widely recognized as a vital and sustainable strategy for mitigating anthropogenic carbon dioxide emissions and addressing global climate change challenges. However, persistent concerns such as high operational costs and the potential for CO₂ leakage highlight the critical need for a monitoring framework that is both cost-effective and environmentally responsible. This study introduces a global optimization-based methodology aimed at designing an efficient CO₂ monitoring program that simultaneously addresses two key objectives: Minimization of monitoring costs, and Enhancement of environmental safety through early detection of CO₂ leakage and accurate subsurface imaging. To achieve this, the study employs advanced global optimization algorithms, including Hybrid Harmony Search (HHS), to optimize the subsurface geophysical model and accurately estimate P-wave impedance, a key parameter in CO₂ monitoring. The proposed optimization framework is validated using a real-world Carbon Capture and Storage (CCS) project at the Sleipner field, which involves CO₂ storage in a saline aquifer. The case study demonstrates that global optimization techniques significantly improve the precision and efficiency of subsurface model calibration, leading to more effective monitoring strategies.