Probabilistic 3-D time-lapse inversion of magnetotelluric data: application to an enhanced geothermal system

摘要: SUMMARY Surface-based monitoring of mass transfer caused by injections and extractions in deep boreholes is crucial to maximize oil, gas geothermal production. Inductive electromagnetic methods, such as magnetotellurics, are appealing for these applications due their large penetration depths sensitivity changes fluid conductivity fracture connectivity. In this work, we propose a 3-D Markov chain Monte Carlo inversion time-lapse magnetotelluric data image following saline injection. The estimates the posterior probability density function resulting plume, thereby quantifies model uncertainty. To decrease computation times, base parametrization on reduced Legendre moment decomposition plume. A synthetic test shows that our methodology effective when electrical resistivity structure prior injection well known. centre spread plume retrieved. We then apply strategy an experiment enhanced system at Paralana, South Australia, compare it deterministic inversion. latter retrieves more shallow than actual interval, whereas probabilistic plumes located correct oriented preferential north–south direction. explain data, requires unrealistically with respect model. suggest partly explained unaccounted subsurface heterogeneities from which inferred.