Modelling of Long-term Along-fault Flow of CO2 From a Natural Reservoir

Abstract

Geological sequestration of CO2 requires the presence of at least one competent seal above the storage reservoir to ensure containment of the stored CO2. Most of the considered storage sites are overlain by low-permeability evaporites or mudrocks that form competent seals in the absence of defects. Potential defects are formed by man-made well penetrations (necessary for exploration and appraisal, and injection) as well as (for mudrocks) natural or injection-induced fracture systems through the caprock. These defects need to be de-risked during site selection and characterisation. A European ACT-sponsored research consortium, DETECT, developed an integrated characterisation and risk assessment toolkit for natural fault/fracture pathways. In this paper we describe the DETECT experimental-modelling workflow, which aims to be predictive for fault-related leakage quantification, and its application to a field case example for validation. The workflow combines laboratory experiments to obtain single-fracture stress-sensitive permeabilities; single-fracture modelling for stress-sensitive relative permeabilities and capillary pressures; fracture network characterisation and modelling for the caprock(s); upscaling of properties and constitutive functions in fracture networks; and full compositional flow modelling at field scale. We focus the paper on the application of the workflow to the Green River Site in Utah. This is a rare case of leakage from a natural CO2 reservoir, where CO2 (dissolved or gaseous) migrates along two fault zones to the surface. This site provides a unique opportunity to understand CO2 leakage mechanisms and volumes along faults, because of its extensive characterisation including a large dataset of present-day CO2 surface flux measurements as well as historical records of CO2 leakage in the form of travertine mounds. When applied to this site, our methodology predicts leakage locations accurately and, within an order of magnitude, leakage rates correctly without extensive history matching. Subsequent history matching achieves accurate leak rate matches within a-priori uncertainty ranges for model input parameters.