Field-scale application of a semi-analytical model for estimation of CO₂ and brine leakage along old wells

Carbon capture and geological storage (CCS) operations will require an environmental risk analysis to determine, among other things, the risk that injected CO₂ or displaced brine will leak from the injection formation into other parts of the subsurface or surface environments. Such an analysis requires site characterization including identification of potential leakage pathways. In North America, the century-long legacy of oil and gas exploration and production has left millions of oil and gas wells, many of which are co-located with otherwise good geological storage sites. Potential leakage along existing wells, coupled with layered stratigraphic sequences and highly uncertain parameters, makes quantitative analysis of leakage risk a significant computational challenge. However, new approaches to modeling CO₂ injection, migration, and leakage allow for realistic scenarios to be simulated within a probabilistic framework. Using a specific field site in Alberta, Canada, we perform a range of computational studies aimed at risk analysis with a focus on CO₂ and brine leakage along old wells. The specific calculations focus on the injection period, when risk of leakage is expected to be largest. Specifically, we simulate 50 years of injection of supercritical CO₂ and use a Monte Carlo framework to analyze the overall system behavior. The simulations involve injection, migration, and leakage over the 50-year time horizon for domains of several thousand square kilometers having multiple layers in the sedimentary succession and several thousand old wells within the domain. Because we can perform each simulation in a few minutes of computer time, we can run tens of thousands of simulations and analyze the outputs in a probabilistic framework. We use these kinds of simulations to demonstrate the importance of residual brine saturations, the range of current options to quantify leaky well properties, and the impact of depth of injection and how it relates to leakage risk.