Ensemble smoothing of land subsidence measurements for reservoir geomechanical characterization |
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| Authors: | Domenico Baù Massimiliano Ferronato Giuseppe Gambolati Pietro Teatini Ayman Alzraiee |
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| Affiliation: | 1. Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, U.S.A.;2. Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, Italy |
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| 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. Copyright © 2014 John Wiley & Sons, Ltd. |
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| Keywords: | Land subsidence data assimilation gas storage and recovery |
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