Imaging the reservoir permeability from time-lapse electromagnetic data

Abstract

The waterflooding process has been used to enhance oil recovery to sustain oil and gas production. The water movement in the reservoir must be monitored in the process. We investigate the use of EM methods in such monitoring by detecting changes in the bulk reservoir conductivity caused by water-oil substitution. Changes in conductivity are linked to water saturation changes in the reservoir using Archie’s relation, while the water saturation changes are predicted by a fluid-flow simulator that depends on the changes in the porosity and permeability of the reservoir. We develop a new coupled inversion algorithm for time-lapse EM monitoring with a rock-physics constraint to reduce the uncertainty in the recovered static reservoir properties, i.e., permeability models, and the fluid saturation change as a function of time. Our inversion algorithm assumes the static reservoir models are correlated either linearly or nonlinearly through rock physics relations. We demonstrate the performance of our inversion algorithm using a synthetic model and show that inversion results using coupled inversion reduce the uncertainty of the recovered permeability model.