Bayesian well-test 2D tomography inversion for CO2 plume detection

Abstract The inversion of cross-well time-lapse pressure tests is investigated as a monitoring technique for locating CO2 plumes in the subsurface. Fast inversion methods are possible which treat the gas plume as either a constant-pressure or reduced diffusivity region. However, neither constant-pressure nor diffusivity-barrier approaches are adequate for modelling the full behaviour of the plume dynamics, especially the late time pressurisation of small plumes. The saturation dependence of the flow is dominated by the compressibility contrast, which justifies the use of an approximate mixed-phase flow equation with saturation dependence confined to the compressibility terms. This fast proxy flow model enables the construction of efficient inversion methods using either low dimensional object–based representations of the plume, or voxelised models exploiting gradients from nonlinear adjoint theory. It allows the inclusion both of cross-well and single-well responses. The inversion algorithms are then tested on a simplified three-dimensional full-physics simulation model of CO2 injection, and give a 2D image for the most probable plume location. By using subsets of the full well data, it is shown that the inversion algorithms can detect and locate the plume with only three monitoring wells, but even two monitoring wells have utility given prior information. More certain delineation of the plume geometry occurs as the number of wells increases, and it is most clearly inferred when the testing well geometry fully surrounds the plume.