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Uncertainty in the long-term fate of CO2 injected for geologic carbon sequestration (GCS) is a significant barrier to the adoption of GCS as a greenhouse-gas emission-mitigation for industry and regulatory agencies alike. We present a modeling study that demonstrates that the uncertainty in forecasts of GCS site performance decreases over time as monitoring data are used to update operational models. We consider a case study of GCS in a depleted natural gas reservoir, with CO2 injection occurring over 20 years, with a 50-year post-injection site care period. We constructed a detailed model to generate the actual model output, which is considered synthetic observation data. A series of simpler operational models based on limited data and assumptions about how an operator would model such a site are then run and compared against actual model output at specific monitoring points after one year, two years, etc. The operational model is updated and improved using the synthetic observation data from the actual model at the same time intervals. Model parameter values and model features needed to be updated over time to improve matches to the actual model. These kinds of model adjustments would be a normal part of reservoir engineering and site management at GCS sites. Uncertainty in two key measures related to site performance decreases with time: extent of the CO2 plume up-dip migration, and radial extent of the pressure pulse. This conclusion should help allay the concerns of industry and regulators about uncertainty in long-term fate of CO2 at GCS sites.

Uncertainty in the long-term fate of CO2 injected for geologic carbon sequestration (GCS) is a significant barrier to the adoption of GCS as a greenhouse-gas emission-mitigation for industry and regulatory agencies alike. We present a modeling study that demonstrates that the uncertainty in forecasts of GCS site performance decreases over time as monitoring data are used to update operational models. We consider a case study of GCS in a depleted natural gas reservoir, with CO2 injection occurring over 20 years, with a 50-year post-injection site care period. We constructed a detailed model to generate the actual model output, which is considered synthetic observation data. A series of simpler operational models based on limited data and assumptions about how an operator would model such a site are then run and compared against actual model output at specific monitoring points after one year, two years, etc. The operational model is updated and improved using the synthetic observation data from the actual model at the same time intervals. Model parameter values and model features needed to be updated over time to improve matches to the actual model. These kinds of model adjustments would be a normal part of reservoir engineering and site management at GCS sites. Uncertainty in two key measures related to site performance decreases with time: extent of the CO2 plume up-dip migration, and radial extent of the pressure pulse. This conclusion should help allay the concerns of industry and regulators about uncertainty in long-term fate of CO2 at GCS sites.

The carbon capture and storage (CCS) program at the American Electric Power (AEP) Mountaineer Plant Product Validation Facility (PVF) is a test program to determine the feasibility of on-site capture and geologic storage of carbon dioxide (CO 2 ) at a coal-fired power plant. The injection wells at the AEP Mountaineer Plant are among a select few CO 2 injection and storage wells to reach the post-injection site care stage in the carbon capture and storage project lifecycle. The post- injection site care program has been implemented since the cessation of injection to meet permit requirements, ensure wells demonstrate mechanical integrity, and obtain data needed to demonstrate secure and permanent storage. This program includes reservoir pressure monitoring, CO 2 plume modeling, shallow groundwater monitoring, and regulatory interaction and reporting. This paper will provide an update on these program activities, including data collection and analysis, as well as share lessons learned with the CCS community. [ABSTRACT FROM AUTHOR]