The key to CCS: monitoring, measurement and verification

July 1, 2015

Carbon capture and storage (CCS) projects need to demonstrate, through monitoring, that the injected CO2 is contained within the geological store during and after injection and no unexpected migration has occurred.

In order to achieve this, there is a requirement to create and conduct a comprehensive measurement, monitoring and verification (MMV) plan as an essential component of any proposed CO2 Storage Plan. The MMV plan is site specific and tailored to the individual site characterization and risk assessment.

To develop the subsurface MMV plan, available technologies are identified and ranked on their reliability, efficiency, cost and benefit. The challenge is to optimize the techniques deployed to verify CO2 plume migration and well and reservoir integrity in the storage site over the potential project life in the most cost effective way. Metering of injected CO2 is clearly mandatory, but once the CO2 has passed the perforations and entered the storage formation, geophysical methods must be utilized to monitor the CO2 migration.

AGR has been providing subsurface sutechnologies wilpport to National Grid Carbon (NGC) over the last three years, predominantly on the subsurface characterization of a potential CCS storage site in the southern North Sea, as part of the White Rose project (plans pictured right). This CCS project is one of two full chain commercial-scale demonstration projects selected for potential funding support from the UK Government CCS Commercialisation Programme.

AGR with National Grid, have performed technical feasibility studies for some of the key MMV technologies. From dynamic reservoir models, which show the expected evolution of the CO2 plume over the 20 years of injection, we generated synthetic time-lapse seismic datasets.

Hence, these mimic the expected seismic differences we would anticipate if we were to acquire 3D seismic data before injection starts and after a number of years. Testing the sensitivity of the seismic differences to various site-specific factors, such as seismic noise, increased pressure due to lower injectivity and CO2 migrating in thin or thick layers, demonstrated the robustness of the technique over these scenarios.

The conclusion was that time-lapse seismic proved an effective tool for monitoring CO2 plume migration at this site. The optimal frequency of surveying is tied to key decisions or events in the plume evolution, such as the CO2 rising to the cap-rock and migrating to the crest. Feedback from the monitoring plan is invaluable in history-matching the dynamic simulation model so that it more accurately predicts future behavior of the CO2 in the storage site.

The integrity of the store itself is to be monitored by in-well measurements, but also potentially by remote permanent microseismic monitoring. Prior to CO2 injection, the baseline (background) seismicity of the area would need to be analyzed and verified by placing an array of microseismic detectors on the seabed.

Monitoring of microseismic events during the injection process would provide cap rock integrity assurance and geomechanical model history matching. The microseismic feasibility study deduced and concluded that there would be sensitivity to microseismic events of magnitude -1.1 which would equate to displacement of less than 0.4mm along a length of 10m at the top of the geological storage layer at 1000 m depth. 

Image: From well and seismic data, we build 3D geocellular models which describe the rock types and properties such as Porosity and Net To Gross.
We populate these ‘static’ rock models with brine in the pore space at our initial saturations and pressures. Then we model the dynamic behaviour of the injected CO2 over time.
We can use these dynamic models to compute the expected seismic time-lapse signal, which is the difference between the pre-injection seismic signal (time 1) and after injection (time 2). The Petro-Elastic model links the changing saturations and pressures to changes in velocities and density, and therefore seismic reflection response. With this 4D modelling workflow, we try "what-if" scenarios to test the sensitivity of 4D seismic data before committing to a data acquisition plan.

Peter Rowbotham is Lead Geophysicist with AGR and has a background in Quantitative Reservoir Geophysics and 4D Seismic.



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