Estimating CO2 Leakage Fluxes along Legacy Wells Using a Rapid Physics-Based Screening Approach

Hariharan Ramachandran , Iain De Jonge-Anderson, Benjamin Pullen, Aaron Cahill

Achieving net-zero targets demands the rapid expansion of CO2 geological storage, but legacy wells in mature petroleum provinces represent a significant containment risk when engineered barriers degrade. At the early assessment stage, storage projects need to rapidly bound plausible leakage magnitudes across large legacy-well inventories when integrity data are sparse; however, suitable modelling approaches remain limited. High-fidelity simulations capture leakage physics in detail but are computationally demanding, while simpler analytical models neglect depth-dependent CO2 property variations arising from realistic pressure–temperature gradients. We present QWellRATE, a simplified physics-based screening model for rapidly estimating upper-bound steady-state CO2 leakage fluxes along legacy wellbore pathways. The model evaluates depth-dependent CO2 density and viscosity using the Span–Wagner equation of state, represents leakage pathways as an equivalent Darcy continuum, and operates on readily available inputs without requiring coupled reservoir simulation. Predicted flux magnitudes are consistent with published field analogues from abandoned wells across configurations ranging from intact multi-plug systems to unplugged pathways. Cement plug permeability is the primary control on leakage flux: two intact cement plugs reduce flux by approximately 760-fold relative to an unplugged well, while simultaneous degradation of all three plugs produces an approximately 500-fold increase. The model’s computational efficiency enables sensitivity analysis, uncertainty quantification, and regional spatial screening. Uncertainty quantification via Latin Hypercube Sampling yields a five-order-of-magnitude flux range driven by multiplicative interactions between degraded cement and elevated mud permeability. Regional demonstration in the Southern North Sea illustrates how QWellRATE can support tiered workflows by prioritising legacy wells for targeted follow-up using high-fidelity multiphase simulators.