Heat Transfer Through the Wairakei‐Tauhara Geothermal System Quantified by Multi‐Channel Data Modeling. Issue 8 (23rd April 2021) - Ardid, Alberto; Archer, Rosalind; Bertrand, Edward; Sepulveda, Fabian; Tarits, Pascal; Dempsey, David
Abstract: To obtain the fullest picture of geothermal systems, it is necessary to integrate different types of data, for example, surface electromagnetic surveys, lithology, geochemistry, and temperature logs. Here, by joint modeling a multichannel data set we quantify the spatial distribution of heat transfer through the hydrothermally altered, impermeable smectite layer that has developed atop the Wairakei‐Tauhara system, New Zealand. Our approach involves first constraining magnetotelluric inversion models with methylene blue analysis (an indicator of conductive clay) and mapping these onto temperature and lithology data from geothermal wells. Then, one‐dimensional models are fitted to the temperature data to estimate heat flux variations across the field. As a result, we have been able to map the primary seal that insulates the geothermal reservoir and estimate the heat flow of the system. The approach could be applied in geothermal provinces around the world with implications for sustainable resource management and our understanding of these magmatic systems. Plain Language Summary: In this study, we present a joint modeling study of a rift‐hosted geothermal field with the goal of quantifying first‐order components of the hydrothermal system: the smectite clay cap, temperature distribution, and heat flux. Our modeling integrates electromagnetic, lithological, and temperature data using 1D models and a stochastic framework that allows uncertainty quantification. A multichannel approach helps avoid misinterpretation with single data streams. This study is significant because it quantifies key heat flows within a complex magmatic‐volcanic‐hydrothermal province. From a practical perspective, this work can help geothermal resource decision makers decide where to drill new wells or develop better models to optimize sustainable electricity generation. Key Points: Bayesian joint inversion of magnetotelluric and clay content data to infer the clay cap boundary Modeling of inferred clay cap and well temperature data to estimate clay formation temperatures and avoid misinterpretation of conductor nature Modeling of well temperature data to inferred thermal gradients and heat fluxes through the geothermal system