Dynamic Characterization of Reservoirs Constrained by Time-Lapse Prestack Seismic Inversion

Dynamic changes of reservoir parameters within a reservoir are usually estimated by a history matching process based on the production data. However, it is difficult to accurately acquire the spatial distribution using production data as the constraints. We formulate a nonlinear inversion method to dynamically characterize the reservoir parameter variations from time-lapse prestack seismic data. Besides, we introduce the modified Hertz–Mindlin (H-M) model to simulate the changes in the elastic behavior of a turbidite sandstone during production by establishing a link between reservoir parameters and elastic parameters. Then, the exact Zoeppritz equation is exploited as a forward engine to convert these parameters into synthetic time-lapse seismic data. Numerical examples verify that the porosity parameter has a higher reflection sensitivity than the other two parameters, which is followed by the effective pressure. The water saturation parameter, however, is the least sensitive. Following a Bayesian approach, the baseline and monitor seismic data are used in the seismic inversion to establish the regularized augmented function. Combining the modified H-M rock physics model with the exact Zoeppritz equation as a forward operator, a regularized function is minimized with the Gauss–Newton optimization method to determine a model update. We further apply the proposed inversion algorithm to a real seismic data set, including baseline and monitor seismic data. The results demonstrate that the proposed inversion method can not only yield an accurate description of subsurface static reservoir properties but also improve the accuracy of dynamic reservoir parameter characterization.