Electrical resistivity tomography (ERT) is a well-established method for
geophysical characterization and has shown potential for monitoring
geologic CO2 sequestration, due to its sensitivity to electrical
resistivity contrasts generated by liquid/gas saturation variability. In
contrast to deterministic inversion approaches, probabilistic inversion
provides the full posterior probability density function of the
saturation field and accounts for the uncertainties inherent in the
petrophysical parameters relating the resistivity to saturation. In this
study, the data are from benchtop ERT experiments conducted during gas
injection into a quasi-2D brine-saturated sand chamber with a packing
that mimics a simple anticlinal geological reservoir. The saturation
fields are estimated by Markov chain Monte Carlo inversion of the
measured data and compared to independent saturation measurements from
light transmission through the chamber. Different model
parameterizations are evaluated in terms of the recovered saturation and
petrophysical parameter values. The saturation field is parameterized
(1) in Cartesian coordinates, (2) by means of its discrete cosine
transform coefficients, and (3) by fixed saturation values in structural
elements whose shape and location is assumed known or represented by an
arbitrary Gaussian Bell structure. Results show that the estimated
saturation fields are in overall agreement with saturations measured by
light transmission, but differ strongly in terms of parameter estimates,
parameter uncertainties and computational intensity. Discretization in
the frequency domain (as in the discrete cosine transform
parameterization) provides more accurate models at a lower computational
cost compared to spatially discretized (Cartesian) models. A priori
knowledge about the expected geologic structures allows for
non-discretized model descriptions with markedly reduced degrees of
freedom. Constraining the solutions to the known injected gas volume
improved estimates of saturation and parameter values of the
petrophysical relationship. (C) 2014 Elsevier B.V. All rights reserved.