Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/104489
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Type: Journal article
Title: Probabilistic 3-D time-lapse inversion of magnetotelluric data: application to an enhanced geothermal system
Author: Rosas-Carbajal, M.
Linde, N.
Peacock, J.
Zyserman, F.
Kalscheuer, T.
Thiel, S.
Citation: Geophysical Journal International, 2015; 203(3):1946-1960
Publisher: Oxford University Press
Issue Date: 2015
ISSN: 0956-540X
1365-246X
Statement of
Responsibility: 
M. Rosas-Carbajal, N. Linde, J. Peacock, F.I. Zyserman, T. Kalscheuer and S. Thiel
Abstract: Surface-based monitoring of mass transfer caused by injections and extractions in deep boreholes is crucial to maximize oil, gas and geothermal production. Inductive electromagnetic methods, such as magnetotellurics, are appealing for these applications due to their large penetration depths and sensitivity to changes in fluid conductivity and fracture connectivity. In this work, we propose a 3-D Markov chain Monte Carlo inversion of time-lapse magnetotelluric data to image mass transfer following a saline fluid injection. The inversion estimates the posterior probability density function of the resulting plume, and thereby quantifies model uncertainty. To decrease computation times, we base the parametrization on a reduced Legendre moment decomposition of the plume. A synthetic test shows that our methodology is effective when the electrical resistivity structure prior to the injection is well known. The centre of mass and spread of the plume are well retrieved.We then apply our inversion strategy to an injection experiment in an enhanced geothermal system at Paralana, South Australia, and compare it to a 3-D deterministic time-lapse inversion. The latter retrieves resistivity changes that are more shallow than the actual injection interval, whereas the probabilistic inversion retrieves plumes that are located at the correct depths and oriented in a preferential north–south direction. To explain the time-lapse data, the inversion requires unrealistically large resistivity changes with respect to the base model. We suggest that this is partly explained by unaccounted subsurface heterogeneities in the base model from which time-lapse changes are inferred.
Keywords: Inverse theory; probability distributions; non-linear electromagnetics; hydrogeophysics
Rights: © The Authors 2015. Published by Oxford University Press on behalf of The Royal Astronomical Society.
RMID: 0030041817
DOI: 10.1093/gji/ggv406
Appears in Collections:Earth and Environmental Sciences publications

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