Utilization of multiobjective optimization for pulse testing dataset from a CO₂-EOR/sequestration field

In a geological carbon storage project, leakage should be monitored to ensure safe long-term storage of injected CO₂. Leakage can be detected early and cost-effectively by monitoring subsurface pressure. The uncertainty in geological models also needs to be sufficiently reduced to detect leakage based on pressure monitoring data. This study presents numerical results of field pulse testing experiments that are designed to detect leakage based on pressure monitoring data for periodical CO₂ injection at a CO₂ enhanced oil recovery field in Mississippi, USA. In the pulse test, sinusoidal pressure patterns are captured in transitional pressure data because CO₂ injection and shut-in are repeated. The patterns are parameterized and history-matched efficiently in the frequency domain. Sensitivity analyses of pulse test parameters such as injection period and rate show that the frequency domain is more advantageous than the time domain for estimating leakage probability and well connectivity. We also conduct multi-objective history matching of pulse testing parameters in the frequency domain for reducing the uncertainty in geological models. This history matching reveals a clearer trade-off relationship between the matching qualities than conventional global-objective history matching, thereby being advantageous to yielding converged and diversified geological models for uncertainty quantification.