Integration of geoscientific data and uncertainties in techno-economic forecasting on CO2 capture and storage

Global warming and the associated climate change can, with overwhelming evidence, be directly linked to the increasing emission of greenhouse gasses by human activities, with CO2 being the most important one. The increase of the mean global temperature has many effects on the natural environment, of which some are considered to be very harmful to the current human society. Under the 2015 Paris agreement, it was therefore internationally agreed  to limit warming to 2°C and even pursue a limit of 1.5°C increase above pre-industrial levels (ca. 1750). These goals are very ambitious and all available emission abatement technologies will need to be deployed to reach them at an acceptable cost to society.

To reduce the emissions from large stationary sources such as power plants and industrial facilities, the CO2 from the combustion of fossil fuels or chemical processes can be captured and stored in geological formations. The CO2 is injected in the naturally occurring porosity of such a subsurface reservoir, where several processes ensure it remains there for indefinite time. Although this process of CO2 capture and geological storage (CCS) is sometimes contested, it has a very large emission reduction potential, and it is likely impossible to reach the 1.5°C goal without it.

Before storage, CO2 can also be put to good use, presenting a potential business case for CCS. Large quantities of CO2 can, for example, be injected into an oil reservoir to enhance oil production. There are several candidate fields for such CO2-enhanced oil recovery (CO2-EOR) in the North Sea, but the economic and practical context is challenging, and no such investments have been made yet.

As climate mitigation and large industrial activities require large investments, assessments of the long-term consequences of policy decisions and technology deployment are carried out with techno-economic computer simulators. For the geological storage of CO2, the true nature of reservoirs and the uncertainties inherently surrounding them are usually neglected in such simulations. This results in unrealistic probabilities of the outcomes, supporting unfavourable decisions on investments and energy and climate policy.

An entirely new methodology has therefore been developed as the PSS suite (Policy Support System) of geo-techno-economic forecasting simulators. A unique combination of algorithms is built and gathered to explicitly include different sources of uncertainty in the forecasting process. A succession of decisions under uncertainty is realistically simulated by making nested Monte Carlo calculations in a Real Options decision tree with true limited foresight. The application of Modern Portfolio Theory ensures an optimal balance between value and risk of investments.

The PSS suite provides two simulation modes. In its first mode, the economy-wide deployment of CCS technology is simulated. CO2 producing facilities are linked through a pipeline network with geological storage reservoirs or export options. In the second mode, investment decisions for applying CO2-EOR and CO2 storage in oil fields are simulated from a company's perspective.

Belgium and Austria are considered as CCS case studies, each having a very different context for the deployment of the technology. In Belgium, potential storage reservoirs are located in deep aquifers, coal sequences and abandoned mines. Austria has potential storage capacity in several (nearly depleted) oil and gas reservoirs. The level of uncertainty on the reservoir parameters prevents from performing realistic and detailed source-sink matching with classic calculations as a high level of reservoir knowledge is supposed to be available when making an investment decision for CCS.

Reservoir uncertainties spanning multiple orders of magnitude and low data availability are difficult to deal with when making detailed source-sink matching. If uncertainties are embraced and fully included in the process, reliable assessments can be made. The developed PSS tools enable making forecasting simulations for CO2 storage and CO2-enhanced oil recovery, while integrating different sources of uncertainty and quantifying their influence.

By collecting and using expert input on the reservoir characteristics a full image of the knowledge and uncertainties can be obtained in the form of probabilistic distributions. An exploration step is simulated, producing a probabilistic result of the available, or practical, capacity. This reservoir data serves as input data for the techno-economic PSS simulations, resulting in an exploration priority list and an assessment of the capacity that will be used for storage, or matched capacity. For Belgium an average practical capacity of 625 Mt is calculated, and the Neeroeteren Formation appears the reservoir most likely to be developed for CO2 storage. For Austria, the average practical capacity is 118 Mt, and the Shönkirchen Übertief reservoir is the most prospective.

Three North Sea oil fields, Buzzard, Claymore and Scott, are analysed in a CO2-EOR case study. An additional CO2 storage phase when oil production has ceased increases project flexibility and reduces investment risk under uncertainty. This contributes to the fact that a lower hurdle rate, which is applied for risk management at the evaluation stage, is necessary compared to what is generally used for these high-risk investments to offset risk.

Flexibility and uncertainty need to match for projects to emerge and potentially fast-track the deployment of CO2 storage in Europe. Profitable enhanced oil recovery projects are possible at oil prices below 50 €/bbl, but a high CO2 price is needed. While techno-economic circumstances largely determine the viability of a project, reservoir performance uncertainty also becomes a determining factor at these economic threshold levels.