Bayesian MT inversion using structural priors for imaging shallow conductors in geothermal fields

Presenter: Alberto Ardid Segura

Date: 18th August 2020

Title: Bayesian MT inversion using structural priors for imaging shallow conductors in geothermal fields

Abstract: Geothermal fields are usually explored by magnetotelluric (MT) surveys primarily to characterize a shallow conductor reflective of a conductive clay structure, commonly known as the clay cap. Standard deterministic MT inversions suffer from non-uniqueness and uncertainty, and the inclusion of useful lithological information is still limited. We develop a Bayesian 1D inversion method that integrates the electrical resistivity distribution from MT surveys with Methylene Blue (MeB) data, an indicator of conductive clay distribution in geothermal wells. The inversion seeks to infer under uncertainty the shallow conductor boundaries in geothermal fields. By incorporating borehole information, our inversion reduces non-uniqueness and then explicitly represents the irreducible uncertainty as estimated depth intervals for clay cap boundaries. This is particularly important when constraining the lower conductor boundary, as this feature is difficult to discriminate from the MT alone. We apply the methodology to a set of 250 MT stations and 130 MeB profiles in the New Zealand Wāirakei geothermal field to estimate under uncertainty the conductor boundaries. Then, we compare the infer boundaries with the clay distribution, temperature logs and lithology from wells to estimate temperature gradients and conductive heat flux through the clay cap. By quantitative correlations among the different data sets, we present an unprecedented view into clay capping structures in high-temperature liquid dominated geothermal fields.

Bio: Alberto is a MSc geophysicist who studied at the University of Chile, and is a current Doctoral candidate at the Geothermal Institute in the University of Auckland. His doctoral research is focussed on studying the electrical resistivity distribution in geothermal fields through Bayesian magnetotelluric inversions that allows assimilating data from different properties such as lithology and temperature, and quantifying uncertainty. Prior to that, Alberto’s research focused on shallow active and passive seismic exploration on geothermal systems. He also has industry experience mostly related to R+D in direct current, gravity and magnetic geophysical methods for mining and basin research.