Ensemble smoothing of land subsidence measurements for reservoir
geomechanical characterization
D. Bau', A. Alzraiee
Dept. Civil and Environmental Engineering, Colorado State University,
CO
M. Ferronato, G. Gambolati, P. Teatini
Dept. of Civile, Environmental and Architectural Engineering,
University of Padova, Padova, Italy
ABSTRACT
Geomechanical models are often used to predict the impact on land surface of fluid withdrawal from deep
reservoirs, as well as investigating measures for mitigation. The ability to accurately simulate surface
displacements, however, is often impaired by limited information on the geomechanical parameters
characterizing the geological formations of interest. In this study, we employ an ensemble smoother, a data
assimilation algorithm, to provide improved estimates of reservoir parameters through assimilation of
measurements of both horizontal and vertical surface displacement into geomechanical model results. The
method leverages the demonstrated potential of remote sensing techniques developed in the last decade to
provide accurate displacement data for large areas of the land surface.
For evaluation purposes, the methodology is applied to the case of a disk-shaped reservoir embedded in a
homogeneous, isotropic, and linearly elastic half space, subject to a uniform change in fluid pressure.
Multiple sources of uncertainty are investigated, including the radius, R, the thickness, h,
and the depth, c, of the reservoir; the pore pressure change, Δp; porous medium's vertical
uniaxial compressibility, cM, and Poisson's ratio, ν, and the ratio, s,
between the compressibilities of the medium during loading and
unloading cycles. Results from all simulations show that the ensemble smoother has the capability to
effectively reduce the uncertainty associated with those parameters to which the variability and the spatial
distribution of land surface displacements are most sensitive, namely, R, c,
cM, and s. These analyses
demonstrate that the estimation of these parameters values depends on the number of measurements
assimilated and the error assigned to the measurement values.
